# Mental health and residential adaptations during the pandemic: a mixed-methods study of working mothers

Pablo Garcia de Paredes, Thierry Ramadier, Marie Baron, Annie Leblanc, Carole Després

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Pablo Garcia de Paredes, Thierry Ramadier, Marie Baron, Annie Leblanc, Carole Després. Mental health and residential adaptations during the pandemic: a mixed-methods study of working mothers. Cities & Health, 2025, pp.1-23. 10.1080/23748834.2025.2496557. halshs-05272997v2

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# Mental health and residential adaptations during the pandemic: a mixed-methods study of working mothers

Pablo Garcia de Paredes, Thierry Ramadier, Marie Baron, Annie LeBlanc & Carole Després

**To cite this article:** Pablo Garcia de Paredes, Thierry Ramadier, Marie Baron, Annie LeBlanc & Carole Després (20 May 2025): Mental health and residential adaptations during the pandemic: a mixed-methods study of working mothers, Cities & Health, DOI: 10.1080/23748834.2025.2496557

**To link to this article:** https://doi.org/10.1080/23748834.2025.2496557

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Published online: 20 May 2025.

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# Mental health and residential adaptations during the pandemic: a mixed-methods study of working mothers

Pablo Garcia de Paredes IDa,b, Thierry Ramadier IDc, Marie Baron IDb, Annie LeBlanc IDb,d and Carole Després Id,a,b

ªÉcole d'architecture et Centre de recherche en aménagement et développement (CRAD), Université Laval, Québec, Canada; bVITAM Research Center on Sustainable Health, Centre Intégré Universitaire de Santé et de Services Sociaux de la Capitale-Nationale, Québec, QC, Canada; cCentre national de la recherche scientifique, Université de Strasbourg, Québec, QC, Canada; dDepartment of Family and Emergency Medicine, Université Laval, Québec, QC, Canada

## ABSTRACT

In the Spring of 2020, the COVID-19 pandemic transformed dwellings worldwide into focal points of daily life. This mixed-methods study examines how women in various residential situations adapted when faced with pandemic challenges and how their adaptations influenced the meanings of home and mental health. A pool of 538 working mothers was identified from Quebec's MAVIPAN repeated measures survey to test if their residential situations, marked by their multiple social roles as mothers, workers, wives, or teachers, and various housing conditions, were associated with mental health and their meanings of home. Four profiles of residential situations were identified through multiple factor analysis (MFA), and 33 women belonging to these profiles were interviewed. Quantitative explorations and ChatGPT-4-assisted thematic analysis revealed conditional associations linking vulnerable residential situations to adaptation difficulties, low mental health scores, and negative feelings toward home, applying to women in blended or single-parent families, in small dwellings, or self-employed.

## ARTICLE HISTORY

Received 30 April 2024
Accepted 17 April 2025

## KEYWORDS

Residential situations;
mental health; the concept
of home; residential
adaptations; COVID-19;
working mothers

## Introduction

The COVID-19 pandemic increased pressure on individuals due to the multiple labor and family life adaptations necessary to maintain work-family balance for 3 years (U.S. Department of Health and Human Services, 2023). With lockdowns, the time spent inside the dwelling, the variety of activities performed, and the presence of family members increased for many households. If the impact of the pandemic on mental health has been well-documented, few examine how these effects manifest in various housing contexts, particularly from the perspective of overlapping vulnerabilities and adaptation pathways. Studies report that people developed feelings of crowding (Watson et al. 2021, Fornara et al. 2022, Park et al. 2023), lack of privacy (Katsabian 2022, Mejía-Castillo et al. 2023) including low digital privacy (Malinao and Sotto 2022), and residential insecurity (Bonacini et al. 2021, Bower et al. 2021, Robinson et al. 2021), impacting their mental health.

Feelings toward home have proven to be a gateway to explaining people's behavior in different life

circumstances and their health implications, like aging at home (Després and Lord 2005, Lord et al. 2017, Barry et al. 2018) or during migration (Alam et al. 2020, Boccagni 2014; Liu 2014). Gender is also known to influence the way home is represented (Bonvalet and Gotman 1993, Saugeres 2009, Doan 2010, Offer and Schneider 2011, Beebeejaun 2017). At the base of the concept of home is the realization that physical housing evaluations like residential satisfaction have connections that are impossible to separate from a symbolic home (Garcia de Paredes 2022). Home is a meta-concept integrating multiple dimensions of people's experiences and representations of housing (Belcher and Vazquez-Calcerrada 1972, Korosec-Serfaty 1985, Després 1991, Moore 2000, Perkins and University of Canterbury, & Lincoln University (Canterbury, N. Z.) 2002, Mallett 2004, Searle and Domènech 2004, Cooper Marcus 2006, Graham et al. 2015, Garcia de Paredes 2020). In the aftermath of the most important health crisis of the last 100 years, how did housing support working women in their adaptation process in the context of successive lockdowns with workplaces and schools

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closed? Did feelings toward their home change 3 years after the beginning of the pandemic? If so, how is it linked to mental health?

This paper reports on a mixed-methods study, building upon a prior quantitative analysis that evaluated the influence of housing-related variables on depression, anxiety, stress, and well-being through validated mental health indicators (Garcia de Paredes et al. 2024). Three sets of factors, identified through a literature review and related to housing attributes, individual and household characteristics, and occupation profiles, were recognized to define residential situations, some of which were found to influence mental health conditions. This first study analyzed data provided by the longitudinal survey *Ma vie et la pandémie au Québec* (My life and the pandemic in Quebec) (LeBlanc 2021), examining more precisely the links between the residential situation and mental health of 1550 respondents who had answered the survey at least three times through different questionnaires, with repeated mental health measures since the spring of 2020. This study aims to understand how successive lockdowns dynamically impacted women's mental health in diverse residential situations in relation to their multiple roles as partners, mothers, homemakers, and professionals. It seeks to shed light on their adaptation strategies during these challenging times and to explore the impact of their experience on the meanings of home.

# Material and methods

## Reviewing the evidence

With the COVID-19 crisis, many researchers argued that the experience of home was potentially threatened, namely by confinement and telecommuting (Byrne 2020, Gurney 2020, Kaufmann et al. 2020). A systematic literature review led to 15 relevant empirical studies investigating housing, mental health, and the experience of home during the pandemic.

Five studies explore the intersection of housing, home, and gender, reporting an intensification of the residence as a gendered space (Cockayne 2021, Andrada-Poa et al. 2022, Heilman and Calarco 2022, Pasqualini et al. 2022, Walters et al. 2023). One study reports on the burden of household chores and childcare carried by women (Pasqualini et al. 2022). Another offers reflections on the relationship between the concept of home and society during the pandemic (Byrne 2020), while the last one highlights the relevance of this concept for social work in times of crisis (Buckland 2021). Three additional papers focus on the

material and psychological dimensions of home (Meagher and Cheadle 2020, Durnová and Mohammadi 2021, Azevedo et al. 2023). Others concentrated on dwelling space to better understand gender inequalities, telework, and childcare for white North American households (Cockayne 2021) and migrants (Hadjiyanni 2023). Finally, the neighborhood and near-home territories were studied from the point of view of the social and leisure opportunities they offer (Mouratidis and Yiannakou 2022) and their contribution to the feeling of being at home (Kuurne Ketokivi and Gómez 2019a, Jellard and Bell 2021). These studies raise doubts about the harmless nature of stay-at-home orders, especially for the most socio-economically vulnerable, including women and single mothers. In continuity with pre-pandemic studies, they confirm the fragility of work-life balance, as well as the myriad routine adjustments required (Hilbrecht et al. 2008, Offer and Schneider 2011).

Adaptations during COVID-19 were found to depend on household combinations of available resources and housing attributes, which are affected by socioeconomic inequalities, like household composition differences (Bonacini et al. 2021, Bower et al. 2021, Robinson et al. 2021) and the gendered division of domestic labor and childcare (Offer and Schneider 2011, Morin et al. 2018, Craig 2020). The interaction between housing type and household composition also affects mental health outcomes (Achou et al. 2020, Doucet et al. 2021, Robinson et al. 2021). Housing attributes are fundamental due to the widespread turn to telework (Dey et al. 2021, Tavares et al. 2021) and distance learning (Andrew et al. 2020, Madrigal and Blevins 2021). They stand as a key element in most participants' accounts of confinement (Bettaieb and Alsabban 2020, Aramayona and Nofre 2021), amplifying the negative mental health outcomes resulting from gender inequalities for parents of young children (Fuller and Qian 2021). Employment modality, and in particular the vulnerability of part-time and self-employed workers, which features in the literature on housing and mental health (Xiao et al. 2022), had a stronger negative influence on depression measures and the deteriorating financial conditions of self-employed women (Beland et al. 2020, Graeber et al. 2021, Kalenkoski and Pabilonia 2022, Caliendo et al. 2023, Miller and Essex 2023).

The COVID-related closing of the majority of work and education places was not only found to intensify the use of domestic space (Hassan and Daniel 2020, Dimopoulos et al. 2021, Susilowati et al. 2021, Martin et al. 2023) but also of digital communication (Kaufmann et al. 2020, Datta and Deb 2021, Jiang
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and Stylos 2021). Teleworkers used mediated technologies in one specific place of the dwelling, namely their home office or a modified living room (Walters et al. 2023), while others alternated spaces, which in turn affected domestic activity patterns (Azevedo et al. 2023). The intensity of people's use of digital technology was found to be linked to their digital literacy (Nash 2020, Rodríguez-de-Dios et al. 2016) but also to their type of engagement with media (Misra and Stokols 2012). These two conditions are likely to influence how digital technology can be used as an adaptation resource (Church et al. 2010, Crovi Druetta 2020, Hardley and Richardson 2021). Coincidentally with regard to technology use, recent studies point to the complications and value negotiations of space sharing of parents with their children during lockdowns (Pitsikali et al. 2023).

The study described below was designed to understand how gender, work, and housing vulnerabilities, e.g. being a renter for low-income mothers (Rolfe et al. 2023) or being a single mother living in small dwellings (Andrada-Poa et al. 2022), can harm mental health and alter the meanings of home. The research questions are: what adaptation strategies were put forward to balance working mothers' overlapping roles during the pandemic? How did these strategies differ among those living in larger and smaller housing units? Or for those working full and part-time? How do digital communications assist or hinder adaptation processes during lockdowns? To what extent did the overall pandemic experience affect the meanings attached to the home and mental health?

## Working hypothesis

The relationship between housing and mental health during COVID-19 was initiated by a quantitative study, where the potential influence of 15 variables on mental health – as identified in the literature review discussed above – was tested. Results indicate that: (1) dwelling occupation density is a predictor of stress and well-being severity; (2) having children under 9 years old and living in apartments is associated with higher stress severity; (3) income and work modality variations conditionally influence the associations between dwelling occupation density and stress and well-being severity (Ibidem). Based on this evidence, a follow-up mixed-method study was designed targeting working mothers with at least one child living with them and who teleworked at some point during the pandemic. More specifically, it examines if and how their residential situations influence their mental health, but

also their capacity to adapt during the pandemic, as well as the meanings of home 3 years after it started.

The following working hypothesis and sub-hypotheses were formulated:

Working mothers with at least one young child living with them adapted to lockdowns using available physical, financial, temporal, and social resources influenced by their residential situations.¹ They experienced adaptation difficulties during successive lockdowns, deteriorating their mental health conditions and their meanings of home. Available interior space, exterior spaces, or multifunctional rooms exemplify physical resources. Financial resources may be used for hiring extra help, food services, or buying home office equipment, while temporal resources are exemplified by flexibility in the organization of time due to flexible work hours or employers who permit work modality changes. Social resources are exemplified by the available help of parents or neighbors or by the opportunity to change jobs due to extensive social networks or personal influence.

* Sub-hypothesis A: Those with the most adaptation difficulties made cognitive adjustments to their representations of what their home should be like, which in return is associated with poorer mental health conditions.

* Sub-hypothesis B: Their digital literacy and attitudes toward digital technology influenced their adaptation strategies.

## Conceptual framework

To further develop these hypotheses, the concept of 'residential situation' was defined as combining occupation profiles, individual and household characteristics, and housing attributes. It endorses the premises of the social-ecological paradigm developed in environmental psychology by concentrating on interactions and relationality, emphasizing the environmental, human, and non-human aspects constitutive of situations (Lewin 1939, Barker 1968, Hall 1976, Bronfenbrenner 1979, Stokols 1995, Wells and Evans 2003, Stokols et al. 2013). At the intersection of gender, residential situations, and mental health is the concept of adaptation, defined from a psychological perspective as the 'process that surrounds the incesant interaction between man [sic] and the dynamic world in which he evolves and interacts' (Jakubowicz, 2002, cited by Simonet 2009, p. 368). Finally, the concept of home completes the framework. Home is a multidimensional construction, a meta-concept that encompasses multiple psychological, social,
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economic, material, temporal, and space-time dimensions (Després and Lord 2005, p. 325). Some of these dimensions have changed, as evidenced by research showing the expanded importance of the home's self-representation dimension (Pagani et al. 2021). Particularly, the economic, material, and temporal dimensions, are likely to have been impacted by experiences of lost income (Achou et al. 2020, Beland et al. 2020, Fuller and Qian 2021, Kalenkoski and Pabilonia 2022), material/organizational adaptation difficulties, and family conflicts (Meagher and Cheadle 2020, Bilodeau et al. 2021, Bower et al. 2021, Hardley and Richardson 2021, Liu et al. 2021, Schieman et al. 2021, Sinko et al. 2021, Susilowati et al. 2021, Sladká and Kreidl 2022, Pitsikali et al. 2023).

## Methodology

The mixed-method follow-up study presented here builds upon prior quantitative research (Garcia de Paredes et al. 2024). Among this prior study's participants, 538 were women and had children under 18 living with them; they were considered for this study. In step 1, a multiple factor analysis (MFA) was performed to identify commonalities among residential situations, which led to the identification of four profiles.² In step 2, the 538 women were contacted by email to volunteer for an online qualitative follow-up to understand the meaning of home, 3 years after the start of the pandemic. Thirty-three of the 55 who agreed were interviewed in French via Zoom. The recordings were transcribed and later analyzed using nonparametric statistical analysis and qualitative thematic analysis, assisted by AI

(ChatGPT-4*). Sample selection and the analysis sequence are summarized in Figure 1.

### Step 1: Defining residential profiles

Thirteen variables were considered for the multiple factor analysis (MFA): age, dwelling occupation density, gross household income, employment status, work modality (teleworking full-time, hybrid work, or on-site work), student status, number of children under 9 years old, couple status, family type, recomposed family or not, neighborhood type, housing type (apartments or single-family homes), tenure type and a supplementary variable combining indicators of stress and anxiety categories (see Table C4). The FactoMineR (Husson et al. 2023) package in R was used (Pagès 2015), preceded by initial explorations of variable groupings in the Factoshiny interface (Husson 2023).³ Each participant's highest dimensional score contribution was used to classify the participant within one of four dimensional spaces representative of their residential situation.

### Step 2: Understanding women's experiences of home

A purposive sample of 33 women was selected from the 55 MAVIPAN volunteers (Table 1), based on diversity in family profiles, housing types, and responsiveness to email invitations to ensure timely participation. For the 1-hour interviews, a semi-structured questionnaire, which included open-ended questions and two cognitive tasks, was designed in five sections (see Appendix B): (1) Residential situation, (2) Work and study routines of family members, (3) Uses and adaptations within the dwelling, (4) Digital technology tools and attitudinal changes, and (5) Home and

<figure><img src="image_16.png" /><figcaption>Figure 1. Sample selection and two-step analysis sequence.</figcaption></figure>
<figure><img src="image_17.png" /><figcaption>**Table 1.** Residential situations of sampled mothers from the MAVPAN study (step 1: MFA analysis, *n* = 538) and interviews (step 2: qualitative follow-up, *n* = 33).</figcaption></figure>

<table><thead><tr><th>Residential situation variables</th><th>Categories</th><th>n = 538</th><th>n = 33</th></tr></thead><tbody><tr><td><strong>HOUSING ATTRIBUTES</strong></td><td></td><td></td><td></td></tr><tr><td>Housing type</td><td>Houses</td><td>83% (449)</td><td>76% (25)</td></tr><tr><td></td><td>Apartments</td><td>16% (89)</td><td>24% (8)</td></tr><tr><td>Tenure type</td><td>Owners</td><td>88% (475)</td><td>90% (30)</td></tr><tr><td></td><td>Renters</td><td>12% (63)</td><td>9% (3)</td></tr><tr><td>Housing occupation density ratio (people/room)</td><td>≤0,4</td><td>29% (157)</td><td>21% (7)</td></tr><tr><td></td><td>0,5-0,8</td><td>66% (357)</td><td>73% (24)</td></tr><tr><td></td><td>1,00*</td><td>4% (24)</td><td>6% (2)</td></tr><tr><td><strong>INDIVIDUAL & HOUSEHOLD CHARACTERISTICS</strong></td><td></td><td></td><td></td></tr><tr><td>Median age</td><td></td><td>40</td><td>39</td></tr><tr><td>Household type</td><td>Couples living together</td><td>84% (452)</td><td>76% (25)</td></tr><tr><td></td><td>Single parents</td><td>10% (55)</td><td>3/9% (3)</td></tr><tr><td></td><td>Couples living apart</td><td>5.7% (31)</td><td>2/6% (2)</td></tr><tr><td>Number of children under 9 years of age</td><td>None</td><td>36% (193)</td><td>21% (7)</td></tr><tr><td></td><td>One</td><td>32% (172)</td><td>39% (13)</td></tr><tr><td></td><td>Two or more</td><td>32% (172)</td><td>39% (13)</td></tr><tr><td>Yearly gross household income</td><td>Low (&lt;49 999 $)</td><td>11% (60)</td><td>12% (4)</td></tr><tr><td></td><td>Medium (50 to 99 999 $)</td><td>33% (177)</td><td>27% (9)</td></tr><tr><td></td><td>High (≥100 000 $)</td><td>56% (301)</td><td>45% (15)</td></tr><tr><td><strong>OCCUPATION PROFILE</strong></td><td></td><td></td><td></td></tr><tr><td>Employment status</td><td>Full-time worker</td><td>68% (366)</td><td>51% (17)</td></tr><tr><td></td><td>Part-time worker</td><td>16% (86)</td><td>21% (7)</td></tr><tr><td></td><td>Self-employed worker</td><td>16% (86)</td><td>27% (9)</td></tr><tr><td>Work modality (teleworker, hybrid, or on-site)</td><td>Full-time teleworker</td><td>43% (233)</td><td>39% (13)</td></tr><tr><td></td><td>Hybrid worker</td><td>23% (122)</td><td>15% (5)</td></tr><tr><td></td><td>On-site worker</td><td>34% (183)</td><td>18% (6)</td></tr><tr><td>Student Status</td><td>Maternity or sick leave.</td><td>Not applicable</td><td>27% (9)</td></tr><tr><td></td><td></td><td>11.4% (61)</td><td>9% (3)</td></tr></tbody></table>

control, with during and after the pandemic questions. All but one participant provided a drawing of their floor plan. During the Zoom® interviews, a COVID-19 timeline, Likert Scales, and respondents' architectural floor plans were shared on screen. All interviews took place in June and July 2023. All interviews were conducted by the lead author, along with their full transcription and subsequent quantitative (MFA) and qualitative analysis. The interviews were recorded on Zoom® and transcribed using TRINT®, while statistical analysis was performed in R®.

The last section of the interview was a cognitive task in which participants were asked about their degree of agreement with eight affirmations that translate enduring dimensions of the meaning of home (Després 1991, Moore 2000, Mallett 2004, Graham et al. 2015), with the addition of a new dimension which emerged during the pandemic, home as a place suitable to work (see Table A1). They were rated using Likert scales ranging from 1 to 10. Participants rated each affirmation twice: first according to how they felt during the pandemic confinements and second, at the moment of the interview. To minimize recollection bias, several questions were asked before the cognitive task, prompting participants to actively recall various aspects of their

experience during the confinement periods (2020–2021). Open-ended questions followed the rating for each affirmation, asking namely to justify the similarities or differences between the two scores, generating valuable discursive data. This comparative Likert scales task had been successfully used by Despres (1993). For analytical purposes, the results were first grouped by individual dimensions and then analyzed collectively. For example, the dimension ‘home as a place of security’ was examined as a group, including all participants. The same procedure was applied to each dimension. Subsequently, the total scores for each dimension were aggregated into three variables representing the entire sample: (1) the sum of all home dimension rating scores during the confinement period, (2) the sum of all home dimension rating scores at the time of the interview, and (3) the difference between the two, used as an indicator of change.

Thematic content analysis was performed using ChatGPT-4®, a conversational natural language processing model based on OpenAI (Generative Pre-trained Transformer or GPT). GPT-4.0 series® utilizes the transformer's mechanisms to generate human-like text responses by weighing the significance of words based on training with vast datasets. To ensure the reliability of ChatGPT's analysis, detailed notes were
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taken after each interview and during transcription to conduct an initial qualitative assessment and identify patterns. The output of the language model was then compared with these notes to verify the accuracy of its descriptions (e.g. ensuring that specific housing details belonged to the correct analysis group rather than another or a hallucination) and to assess whether the identified patterns provided a sound approximation of the collected data. In the preparation phase, transcript answers were first anonymized and then broken down according to four profiles of participants' residential situations resulting from the MFA analysis. Each long-format document was introduced in the chat window using ChatGPT Splitter, a tool that automatically divides lengthy texts into smaller parts according to imposed limits. The two following prompts were successively assigned after adjustments had been made in a test phase: (1) name and count the issues participants faced during the pandemic and their corresponding adaptation mechanisms and (2) name the housing-related themes mentioned by participants. While the model produced relatively accurate counts of cases, all results were manually verified against participant profiles. This revealed a notable error margin of approximately 10% in frequency reporting.

# Results

## Four residential situation profiles

The MFA was performed using four variable groupings derived from the initial residential situation variables (Table C5, Appendix B) and according to their nature, either continuous or categorical. Variable grouping 1 is defined by two continuous variables, age and dwelling occupation density, treated symmetrically, meaning they are attributed equal weight. Variable grouping 2 is made of four nominal categorical variables related to occupation profiles: gross family income, employment status, work modality, and student status. Variable grouping 3 includes individual and household characteristics, encompassing three nominal categorical variables: number of children under 9 years of age living in the household, couple status, and family type (if the family is a recomposed family or not). Finally, Variable grouping 4 comprises three housing attributes and nominal categorical variables: neighborhood type, housing type, and tenure type. An additional ordinal indicator capturing nine different combinations of stress and anxiety levels – key mental health aspects affected by residential situations – was tested for its association

with these groups as a supplementary variable not influencing factor reduction.

The MFA analysis identified four principal dimensions accounting for 32.4% of the total variance (see Table C1). Although the Kaiser rule (Kaiser 1960) states that reliable factors have eigenvalues greater than one, and only dimensions 1 and 2 complied with values of 1.54 and 1.32; the other two components marked below one by a very small margin (0.99 and 0.94) and were included due to their relative importance in the total variance explained. Dimension 1 accounts for 10.39% of the total variance, dimension 2 for 8.92%, dimension 3 for 6.7%, and dimension 4 for 6.93%.

MFA Variable grouping 1, comprising continuous variables age and dwelling occupation density, exerts the greatest impact on dimensions 2 and 3, with 53.04% and 62.16%, respectively (see Table C2). It is responsible for only 3.06% of the influence on Dimension 1 and 5.49% on dimension 4. MFA Variable grouping 2, which encompasses occupation profile variables, predominantly influences dimension 4 with a high score of 58%. It has a moderate influence on all first three dimensions, with 25.19%, 23.77%, and 23.38%, respectively. MFA Variable grouping 3, incorporating variables related to individual and household characteristics, has a 43.12% pronounced influence on dimension 1 and a low influence on dimension 2 of 18.84%, and even lower influence on dimensions 3 and 4, with 13.57% and 10.59%, respectively. MFA Variable grouping 4, which includes housing attributes variables, significantly affects both dimensions 1 and 4 through a 28.6% and 25.9% influence, respectively. It has a low influence on dimensions 2 and 3, with 4.35% and 0.89%.

Figure 2 shows the Hexbin plots⁴ of dimensional relations evidenced by the MFA. For the first two dimensions, the Hexbin plot shows a wide spread of data points. Dimensions 1 and 2 explain 19.31% of the variance. Dimension 1 highlights that for certain groups, namely blended or recomposed families or single mothers in apartments and rental units, there is a potential clustering of adaptive challenges, irrespective of the dwelling's occupation density or the age of the participant. Dimension 2 sheds light on common traits among young mothers with children under 9 years of age, emphasizing the significant role of their employment variables. Dimension 3 showed a steep normal curve, while most dimensions had a normal distribution of participants. As for dimension 4, the distribution is very close to normal.

The stress-anxiety index – the supplementary variable combining mental health scores on stress and
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<figure><img src="image_20.png" /><figcaption>Figure 2. Hexbin cross-plots showing dimensional distribution and relationship (n=538).</figcaption></figure>
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anxiety – registered different associations with the four dimensions: dimension three is highly associated (63.15%), while dimension 1 showed a weak association (6.37%). Dimensions 2 and 4 showed no association. Delving into the global contributions of variable groupings and the contribution of individual variables to each dimension, participant residential situation profiles were constructed through their proximity to each dimensional space and later used for content analysis with ChatGPT-4 (For individual variable contributions, see Appendix C).

Residential situation profile 1 (162/538 and 4/33): *Young single-parent or recomposed family mothers living in apartments.* Dimension 1 encapsulates participants navigating high adaptation challenges. It is markedly shaped by young individuals at both ends of the income spectrum, as well as by those in unique family structures such as couples living separately, single mothers, or recomposed families, all predominantly residing in rental loft-studio-type apartments.

Residential situation profile 2 (153/538 and 7/33): *Self-employed mothers with young children.* Dimension 2 reflects the dynamics of young, self-employed workers, often with young children. This dimension is characterized by middle-aged participants with a strong influence of variations in dwelling occupation density, i.e. lower-than-average dwelling occupation densities, the autonomy of self-employment, and to a lesser extent, the dual roles of full-time salaried employment and academic pursuits, with a noticeable influence from having young children.

Residential situation profile 3 (108/538 and 9/33): *Studying and working on-site mothers with children.* Dimension 3 is indicative of participants working on-site with middle Gross Household income (yearly 50,000–79,999 CAD) with a strong presence of student status and children younger than nine and higher-than-average dwelling occupation densities.

Residential situation profile 4 (115/538 and 13/33): *Self-employed middle-income mothers in small dwellings.* Dimension 4 comprises adaptation situations with individuals of moderate-income levels who are self-employed, with an average influence of age and dwelling occupation density, together with a moderate influence of tenancy in rental apartments.

## The meaning of home during and after the confinements

Table 2 shows the mean results for the affirmations on home dimensions, with affirmations 5.3 (place to nurture relationships), 5.4 (having space at home), 5.6 (place for accommodating work, a new dimension included in this study), and 5.7 (protection from the outside) earning the lowest mean scores during the pandemic. The sums of these dimensions were calculated for each participant, including their difference, and then tested for bivariate correlations with mental health indicators.

Upon verification, those who reported greater differences in home dimension scores⁵ generally suffered from greater stress, although the weak 0.242 Spearman correlation in this small sample was not significant (sig. 0.19). In contrast, the sums of home dimension scores at the moment of the interview and during the pandemic are moderately correlated with mental health indices and are significant. For the sum of all home dimension scores during the interview, both well-being (–0.352, sig. 0.048), anxiety (–0.362, sig. 0.046), and stress (–0.366, sig. 0.043) showed associations (see Table C3). For the sum of all dimension scores during the pandemic, only well-being is significantly associated (–0.426, sig. 0.015). Yet when looking independently at the four most affected home dimensions during the pandemic, a place to nurture relationships, space at home, place for accommodating work, and as protection from the outside, a categorical variable counting all participants who answered with low⁶ scores in home as

<table><caption>**Table 2.** Average Likert scale scores (1–10) obtained for each statement on eight dimensions of the meaning of home, as experienced during confinements and at the time of the interview.</caption><thead><tr><th>Home dimension questions</th><th>Mean rating scores (during confinements)</th><th>Mean rating scores (during interview)</th></tr></thead><tbody><tr><td>1. "At home, I feel that I (and my family) are safe".</td><td>8,88</td><td>9,64</td></tr><tr><td>2. "At home, I have all the privacy I (and my family) need".</td><td>7,21</td><td>8,55</td></tr><tr><td>3. "At home, I can thrive in my relationships with the people I love".</td><td><strong>6,88</strong></td><td>9,27</td></tr><tr><td>4. "At home, me and my family have enough space for all our activities".</td><td><strong>6,88</strong></td><td>8,39</td></tr><tr><td>5.5 "At home, me and my family have the freedom to rearrange our home as we wish".</td><td>8,70</td><td>8,85</td></tr><tr><td>5.6 "At home, I live in a place that suits the way I work, either telecommuting or on-site".</td><td><strong>6,97</strong></td><td>8,94</td></tr><tr><td>5.7 "At home, me and my family are protected from the pressures of the outside world".</td><td><strong>6,73</strong></td><td>8,24</td></tr><tr><td>5.8 "At home, me and my family can assert our identity and lifestyles".</td><td>8,55</td><td>9,45</td></tr></tbody></table>
<figure><img src="image_22.png" /></figure>

a place to nurture relationships and home as protection from the outside is significantly associated with mental health severity: the relationship dimension and stress severity have a Spearman correlation of 0.435 (sig. 0.014), while protection/outside has a 0.570 (sig. <0.001) correlation with well-being severity, 0.454 (sig. 0.009) with depression, 0.447 (sig. 0.012) with anxiety, and 0.451 (sig. 0.011) with stress.

Among the 15 participants reporting minimal difference in their home dimensions -those less affected by the pandemic- only two lived in apartments (2/15). The other six apartment residents were among the 18, noting significant differences in their home dimensions (6/18).⁷ Among these participants, three endured severe stress, three were among the top 10 for anxiety severity, two were within the top 10 for depression, and four were in the top 10 for low well-being. Excluding three cases with dramatic health and family events (e.g. winter falls causing concussions, very old age, and a participant enduring her daughter's suicide attempt), the five remaining apartment dwellers ranked in the top six for stress severity, the top three for severe anxiety cases, and within the top five for depression. These apartment dwellers seem more vulnerable than the rest. Table 3 shows the means for each residential situation profile's mental health severity index. Among the profiles of participants who live in single-family houses, profile 2 appears to have the best overall mental health score, coinciding with the lowest dwelling occupation density scores in the sample.

### Residential situation profile 1

This profile corresponds to young mothers with extreme incomes (very low or high) in single-parent or stepfamilies in apartments (n = 4) encountered five recurring problems. First, they struggle to balance telework and childcare, often resorting to adjusting work schedules, like working evenings. Second, the lack of personal space and privacy leads to the creation of schedules to allocate individual time and space. Third, managing online education for children is a key issue, with participants actively participating in their children's education by setting study schedules.

Fourth, social isolation and limited external contact are mitigated through technology and outdoor activities, and five, financial strain in the case of low-income participants is addressed through expense adaptations, such as online shopping and purchasing used items, like used books for children.

Regarding housing-related themes during the pandemic, participants stress five significant points: Limited space in urban apartments, which poses difficulties in creating separate work, study, and leisure areas. The need for outdoor access, such as balconies or courtyards, is deemed essential for mental health, especially for children. Adapting living spaces for multiple functions, like turning dining areas into workspaces or classrooms. Challenges in maintaining a quiet environment for telework, particularly with noisy children, regardless of their age. Lastly, they express preoccupation with the impact of housing on mental health and well-being.

### Residential situation profile 2

Profile 2 is composed of self-employed middle-aged mothers with young children in housing with below-average occupancy density (n = 7) and five major challenges emerged. First, participants frequently grapple with balancing work and childcare. To overcome this, they adjust their work schedules to align with their children's routines. Secondly, the lack of personal workspace emerges as a common concern. In response, they convert various home areas into make-shift offices. Maintaining professionalism during remote work also presents challenges, which participants address by using virtual backgrounds to maintain a polished appearance.

Additionally, they worry about keeping children engaged and educated. Finally, managing work-life balance is a recurring issue, prompting them to establish clear boundaries between work hours and family time. Regarding housing-related themes during the pandemic, spatial reorganization is prevalent. The need for outdoor space, like gardens or balconies, for recreation is emphasized. Participants prioritize creating quiet and private areas. Technological integration becomes crucial, with enhancements in internet services and the setup of tech-equipped home offices.

<table><caption>Table 3. Mental health severity index means by residential situation profiles (n = 33).</caption><thead><tr><td>Residential situation profile</td><td>Mean for mental health severity index</td><td>n</td><td>Standard Deviation</td></tr></thead><tbody><tr><td>1 Young single-parent or recomposed family mothers in apartments</td><td>8,00</td><td>4</td><td>0,00</td></tr><tr><td>2 Self-employed mothers with young children</td><td>6,43</td><td>7</td><td>2,44</td></tr><tr><td>3 Studying and working on-site mothers with children</td><td>7,00</td><td>9</td><td>2,00</td></tr><tr><td>4 Middle-income self-employed mothers in small dwellings</td><td>7,38</td><td>13</td><td>1,56</td></tr><tr><td>Total</td><td></td><td>33</td><td></td></tr></tbody></table>
<figure><img src="image_23.png" /></figure>

Lastly, flexibility and multipurpose spaces are a recurring theme, reflecting the necessity for areas that can transition seamlessly from work to family use.

### Residential situation profile 3

This profile represents mothers of children under nine who study and work on-site, and whose dwelling occupation density is above average (n = 9). Several challenges stand out: on-site work commitments with proper childcare are a significant concern. To address this, participants delegate between partners, apply flexible work hours, and occasionally find external support. The constraints of medium-sized dwellings were mentioned, where parents find limited personal space and privacy. Their response involves creating dedicated work or study areas and scheduling personal time. Managing children's online education was especially challenging for parents with young children. Challenges include financial strain due to increased expenses or reduced income. Mental and physical health are other concerns. Strategies encompass self-care routines, and some participants mentioned regular exercise and allocating time for relaxation activities.

Regarding housing-related themes, participants express a common need for additional space, primarily to create dedicated work and study areas within their homes. They often reorganize rooms and repurpose living areas to accommodate both work and children's education. Privacy challenges in shared spaces prompt participants to use headphones, establish boundaries, and create schedules. They also emphasize the value of outdoor access for relaxation and their children's activities, utilizing balconies, backyards, or nearby parks for breaks and leisure activities. Lastly, the need for reliable internet and technological resources for work and children's online education leads participants to upgrade internet plans and invest in new devices or ergonomic furniture.

### Residential situation profile 4

This last profile includes self-employed, middle-income mothers living in apartments or small dwellings (n = 13), balancing professional autonomy and financial stability. Common issues and strategies emerged. Participants frequently grapple with a lack of dedicated workspace within their apartments, often adapting by creating makeshift workspaces in various areas. To mitigate noise and disturbances, many establish strict work hours and boundaries within their home environments. Additionally, the absence of personal outdoor space is a concern, prompting individuals to seek solace in nearby parks or public areas.

Regarding housing-related themes, participants consistently emphasize space utilization, maximizing the limited space for work, leisure, and family. Privacy and separation within the home are also crucial. Access to outdoor spaces, such as balconies or nearby parks, is frequently mentioned, underscoring the desire for open spaces for those living in small dwellings (Ratiu and Lévy-Leboyer, C., (1928-2015), and Université Paris Descartes (1970-2019) 1996).

## Discussion

Results show the conditional influence of vulnerable residential situations on home conceptualizations and mental health severity for residential situations marked by overlapping vulnerabilities in the case of single or blended family structures, lack of space in small dwellings, younger age, and self-employment. These vulnerabilities were seen especially in Profiles 1 and 4, encompassing 17 cases within the 33-participant sample. In the case of Profile 2, even though it contains self-employed mothers like Profile 4, their lower-than-average dwelling occupation densities seem to help reduce mental health risks. Our study coincides with research emphasizing the pivotal role of dwelling space and flexibility during the pandemic (Bettaieb and Alsabban 2020, Fornara et al. 2022) and portrays the interactions between gender and self-employment (Beland et al. 2020, Kalenkoski and Pabilonia 2022), further evidencing previous studies on gender and housing disadvantage (Saugeres 2009). These inequalities collided with telework (Leroy et al. 2021, Tavares et al. 2021, Katsabian 2022). Gendered social discourse still portrays women as default homemakers, while men focus on work (Armstrong 1997). As this example shows, even though both partners teleworked, this young mother of two children (6 and 1), from profile 4, with higher-than-average stress, anxiety, and depression, had to negotiate working space, generally prioritizing her partner:

> P33: Since my partner has a lot more meetings than I do...I have a lot less. The fact that, at that time (when he's in a meeting), I'll move either to the master bedroom ... if I have a meeting at the same time as him...even on the bed or else on the kitchen island.

Residential situations seem to be associated with augmenting health risks for working mothers, taking a toll on their home conceptualizations, particularly for those home dimensions focused on personal relationships with loved ones, having enough space at home,
<figure><img src="image_24.png" /></figure>

a place suitable for work, and home as protection from the outside world. When living with small children, nurturing relationships with loved ones and having privacy were hard to fulfill, as evidenced by this severely stressed and low well-being mother of two young children (8 and 6) from profile four who moved from her apartment to a single-family detached dwelling:

P18: Like upstairs (of their new detached house, after leaving their old apartment), you know you can forget that the kids are there. Then, the way the living room is designed (in her old apartment), the children's bedrooms overlook the living room; you try to relax, and there (young child) is in his room, looking at you from his bed, you know, (and said): 'what are you doing?' (participant uses very fine voice). Even at night, we didn't have any 'bubble', whereas now we can be in the living room, and the kids can sleep and do their thing upstairs. I think that's what it was; it's harder to have intimacy and to do our own thing.

Low scores for Home as protection from the outside world dimension were highly associated with mental health severity. One could hypothesize that during an exogenous crisis, home's ability to shield people from outside pressures is akin to a measure of the crisis' negative intensity on their daily lives, justifying the strength of the observed associations for vulnerable profiles (Bonacini et al. 2021, Doucet et al. 2021, Robinson et al. 2021).

Depressed participants in our sample experienced changes with an inevitable quality, e.g. some worked in education and had to endure distance teaching while living in small dwellings, and others were self-employed, facing significant financial loss. Teacher's mental health during the pandemic is associated with institutional support systems and the number of stressors in Canada (Gadermann et al. 2023) and with unexpected working schedule changes in the US (Cavallari et al. 2023), which opens the door for the observed conditionalities in the present study, and financial loss is a crucial phenomenon observed among women in Germany (Graeber et al. 2021, Caliendo et al. 2023).

The conditional effects of residential situations on well-being reported here coincide with the previous quantitative study, which included both men and women in the sample. Adaptation difficulties seem to be triggered by the arrival of the COVID-19 crisis's insecure working conditions combined with crowding feelings (Park et al. 2023), widespread telework, sometimes lower incomes, and the presence of young children in a compounded manner, so that more vulnerabilities mean higher mental health risks.

Work-related vulnerabilities are prevalent in self-employed women, some of whom would opt for unconventional hybrid arrangements in normal times (Rodríguez-Modroño 2021), not a possible adaptation during confinements. Self-employed mothers had important adaptation challenges, which sometimes overlapped with the presence of young children and low-to-moderate dwelling occupation densities influencing their well-being, confirming the low well-being links for mothers multitasking at home (Offer and Schneider 2011). The temporal flexibility of multitasking at the 'honeymoon stage' of the pandemic (DeWolfe 2000), together with some international cases of rising well-being at this stage (Recchi et al. 2020), seems to be detrimental to well-being in the medium and long term. Here, a 40-year-old mother of two (aged 9 and 5) from profile 3, living in an apartment at the start of the pandemic before moving to a single-family detached house, a part-time teleworker, explains how her children's room was closer to her working space and how her children would constantly interrupt her instead of her husband:

P32: For (daughter), as she was very small, she needed me to be with her to handle the computer and everything. So, there she was in my office, in my closed room. (Eldest children) He was right next door, but the door was closed. So that was more me (who took care of the kids). Then, at some point, we talked about it with (husband). And he said: 'I was there. It would be nice if I did it (look after the kids) occasionally'. So sometimes (husband) would do it too. But it was mostly me doing it.

Key differences were observed in the adaptation strategies of the four profiles: Profiles 1 and 4 focused on flexible scheduling to allocate individual time and space within their limited living areas, often adjusting work hours and creating personal schedules to find privacy and efficiency. In the case of anxious participants living in unadaptable environments, such strategies are potentially aimed at reducing negative repetitive thinking (Atzeni et al. 2024), which was in part made possible by the fact that they had older children. In contrast, Profiles 2 and 3 engaged in creative strategies to keep their children actively involved in educational activities, sometimes emphasizing delegation between partners for childcare. In the case of profile 3, sometimes they manage to schedule time for relaxation and study.

Some mothers across the four profiles received help from their parents. This was facilitated by proximity, as is also the case in Spain, where '69% of emancipated children live near their parents, within 5 kilometers' (Ayuso et al. 2020, p. 276). These connections were
<figure><img src="image_25.png" /></figure>

mostly the case for highly educated participants coming from equally educated non-working-class families. Their mental health measures were spared when compared to working-class mothers. The use of technology at the dwelling also became problematic for mothers of Profiles 2, 3, and some within Profile 4 with very young children who felt the need to moderate screen time. For the self-employed in Profiles 2 and 4, the main cognitive adaptation was the realization that they were their own safety net. They were left traumatized by the impossibility of operating their businesses during confinements. Intense digital technology use commanded the polarization of views about technology and added a new role for mothers: that of arbiters of technology utilization. The findings also reveal that privacy regulation (Altman 1977) within the home is influenced by the nature of work roles and family complexity, paralleling dynamics observed in the workplace (Kupritz 2020) but accentuated by the multiplicity of converging roles.

Mothers' adaptations were not just strategies to cope with individual stressors but rather a multipurpose response to a confluence of pressures rooted against their social expectations and feelings of home (see Table D1). Residential situations' influence seems conditional and potentially accumulative due to socially constructed perspectives (Lofland 1991, Clarke 2005, Glaser and Strauss 2017). One participant from Profile 4 – a self-employed psychologist working mother of 2 adolescents living in near-crowded conditions expressed the consequences of dealing with constant distractions leading to adaptation difficulties in her small home, coinciding with similar profiles in the UK (Miller and Essex 2023):

P12: So that puts the finger on a change I've observed inside myself, namely the pandemic, which has diminished my optimism and my ability to project myself and reduced my range of possibilities. Before the pandemic, it was. But yes. There's no problem. Of course it will be. ... Because of the pandemic, now I think: 'Okay, one day at a time. As long as it's not 100% sure and certain, it's not 100% sure and certain'. I wasn't like that, and I hate that I'm not like that anymore. ... So, it's clearly an effect of the pandemic to have been cut off.

## Strengths and limitations

One of the strengths of the study is that it combines the analysis of overlapping vulnerabilities with data on the physical characteristics of housing and symbolic perceptions of residential spaces. It emphasizes the importance of residential situation in understanding the impact of housing on mental health. The

conceptual framework and the progressive methodology made it possible to reveal how and why environmental effects are felt more intensely in more at-risk residential situations, with reduced possibilities of adaptation. The use of MFA to guide thematic qualitative analysis proved effective. It enabled the identification of complex patterns while respecting contextual variability.

The research is not without limitations in terms of its methodology. First, the database used was not designed to specifically describe residential situations. Future research should include housing variables like building defects (Foster et al. 2022), home office design features (Srivastava et al. 2024), housing location, neighborhood features (Bonaiuto et al. 2004, Kuurne Ketokivi and Gómez 2019b), as well as additional socioeconomic variables for both partners and children (work, school, or leisure likewise) (Susilowati et al. 2021, Martin et al. 2023), and residential history (Despres 1993, Garcia de Paredes 2025). Second, the MFA posed some problems in accurately categorizing residential situations as the resulting groups often contained internal differences (although revealing the complexity of real-world situations). Finally, if ChatGPT-4* was useful in efficiently processing large amounts of text and identifying patterns linked to adaptability, our experience is that it complements rather than replaces human analysis.

## Conclusion

The results show that negative changes in representations of home have significant associations with poor mental health, clarifying the relationship between vulnerable residential situations and adaptation difficulties (Meagher and Cheadle 2020, Bower et al. 2021, Cockayne 2021, Durnová and Mohammadi 2021, Jellard and Bell 2021, Walters et al. 2023). The study confirms the general working hypothesis and sub-hypothesis, showing that women in vulnerable residential situations are associated with a higher risk of experiencing adaptation difficulties connected to negative feelings of home, and ill mental health in a compounded manner. Dwelling occupation density played a critical role thanks to its conditional association with factors like self-employment or the presence of young children, harming dimensions of the home. Nevertheless, when crowding feelings were detected, their presence added to the already vulnerable residential situation as a conditional factor, eclipsed by housing type among housing attributes in the context of Quebec, coinciding with studies
<figure><img src="image_26.png" /></figure>

on the relationship between control and housing type in Canada and the UK (Dunn 2002, Gibson et al. 2011).

Strong evidence supports hypothesis A: vulnerabilities in residential situations are linked to adaptation difficulties. They accumulate, and when they coincide with events affecting the participants' health, families, or jobs, they expose people to the perfect storm (Carroll et al. 2009). Taking into consideration, the time dimension is another key element: once a disruption like the pandemic takes place, or when a tragic event like health issues or the death of a family member happens, vulnerable residential situations contribute to worsening the participant's mental health through adaptation difficulties that slowly accumulate, damaging their experience of home.

## Notes

1. This paper concentrates on time organization and cognitive adaptations, implying a change in the use and occupation of a room throughout the day or week and revising representations of what a home is or should be. Material adaptations have been the focus elsewhere (Garcia de Paredes et al. 2024), consisting of altering the material properties of the dwelling or its furniture, especially in connection with telework and distance learning during the pandemic.

2. This dimension reduction method weighs tables with different scales or features individually so that continuous or categorical variables do not gain an accidental advantage over the rest during subsequent integration (Rau et al. 2020).

3. Of the 13 variables, 11 had less than 2% missing values, which were imputed based on the mode value. Income and work modality had 17% of missing values, which were imputed proportionally and randomly.

4. Hexbin plots are histogram plots for data visualization in two-dimensional space, in which the bins are hexagons, and the color represents the number of data points within each bin, from 1 (dark purple) to 12 (yellow). They are especially useful for large datasets.

5. Calculated by subtracting their Likert Scale score during the pandemic from their score at the moment of the interview.

6. Lower than five.

7. Out of all participants, eight lived in apartments, coinciding with profiles 1 and 4.

## Disclosure statement

No potential conflict of interest was reported by the author(s).

## Funding

The work was supported by the Fonds de Recherche du Québec-Société et Culture and by VITAM, Centre de recherche en santé durable.

## Notes on contributors

Pablo Garcia de Paredes earned his PhD in Architecture from Laval University (2024), Quebec, Canada, and holds a PhD in Humanities and Social Sciences from the National University of Panama (2023). As a leading urban consultant, his research explores the meaning of home, residential satisfaction, transit-oriented development (TOD), and housing policy.

Thierry Ramadier, PhD, is CNRS research director in environmental psychology, at SAGE laboratory (Societies, Actors, Government in Europe, UMR 7363), Strasbourg (France). Founder of the Cartotête research network on the social dimensions of cognitive mapping, he is interested by the link between socio-cognitive representations of urban space and daily mobilities, to address spatial segregation from the perspective of everyday practices.

Marie Baron, Ph.D., is Equity, Diversity and Inclusion Advisor at Institut national de la recherche scientifique, Quebec, Canada. She has completed her PhD in Community Health at Laval University in 2019, her project focused on healthy aging in Inuit communities. She then worked as a research professional at the Centre de recherche en santé durable VITAM, specializing in the coordination and management of health surveys among vulnerable populations.

Annie LeBlanc, PhD, is a full professor at Laval University, and a regular researcher at VITAM - Centre de recherche en santé durable. She holds a Canada Research Chair in Knowledge Mobilization and Integration in Practice. Her work aims to optimize healthcare delivery while delivering rapid and significant benefits to patients.

Carole Després, PhD, is a professor of architecture at Laval University, Quebec, Canada. She has researched the meaning of home for the last 30 years and directed multiple research groups studying the intersection between housing, architecture, and human behavior.

## ORCID

Pablo Garcia de Paredes ID http://orcid.org/0000-0003-4630-2218

Thierry Ramadier ID http://orcid.org/0000-0001-8531-5412

Marie Baron ID http://orcid.org/0000-0003-1081-2662

Annie LeBlanc ID http://orcid.org/0000-0002-5377-8102

Carole Després ID http://orcid.org/0000-0002-5624-8270

## Ethical approval

This study was approved by the Ethics Committee of the Primary Care and Population Health Research Sector of the Quebec Integrated University Health & Social Services
<figure><img src="image_27.png" /></figure>

Center (CIUSSS de la Capitale-Nationale), reference number: 2021-2043, Committee of record, and the Ethics Committees of participating institutions.

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# Appendices

## Appendix A. Notes on ChatGPT-4 as a qualitative analysis tool

Interactions with ChatGPT do not lead to real-time training and the model doesn't learn or change based on inputs.

The model is part of the field of machine learning and sits within a subfield named generative deep learning models like Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Restricted Boltzmann Machines (RBMs). It is based on the transformer model, which simplifies natural language processing by removing the complex recurrence found in other neural network models with an encoder and a decoder (Vaswani et al. 2017).

Several limitations of ChatPPT-4.0* existing at the time of use should be stressed. They required meticulous data preparation as well as the adoption of validation measures on the part of the researcher: (1) the need to anonymize all information, calling for a rigorous framework for inputting information and task formulation; (2) the need to overcome the imposed limit of 4096 tokens or about 204 seven-line paragraphs of low complexity in one single input window, which required to break down the text to respect this limit, and later, specify to the Chat the relationship between segments; (3) the need to input data each time a new question is asked since the model has no memory, for several reasons including security; and (4) the need for constant and systematic verifications from the researcher using firsthand transcriptions to assure the reliability of the results because ChaptGPT-4.0 * still makes mistakes at semantic tasks (Kocóń et al. 2023) and the training data has probable biases.

## Appendix B. The questionnaire and Likert scale

Likert scale:

<table><caption>Table B1. Questionnaire sections and questions.</caption><thead><tr><td>Section</td><td>Interview questions from our survey instrument</td></tr></thead><tbody><tr><td>1. Work and school day routines</td><td>1.1 What was a typical work/school day like for your family before the pandemic?<br/>1.2 In comparison, what did this schedule look like during this first confinement?</td></tr><tr><td>2. Childcare and food preparation in the dwelling</td><td>2.1 how was home school supervision organized? Who was responsible? Was it different from your pre-pandemic homework habits?<br/>2.2 how were meal preparation and other household chores organized? Was this different from your pre-pandemic habits?</td></tr><tr><td>3. Housing adaptations during the pandemic</td><td>2.3 Did you experience the 2nd period of confinement in the same way? How was it similar or different?</td></tr><tr><td>4. Digital technology at the dwelling, time and material adaptations, and changing representations</td><td>3.1 I'm going to go through the rooms on the drawing one by one, and you're going to tell me where the various members of your family were for distance learning and teleworking in the morning, afternoon and evening (if applicable)?<br/>4.1 how many computers, tablets and smartphones are there in your family? Have you acquired any new ones since the first containment?<br/>4.2 Looking at the architectural plan of your home, where are these fixed computers and, usually, laptops or tablets currently located? Is this different from the periods of confinement?<br/>4.3 To what extent would you say that the use of computers and tablets for leisure, shopping and contact with loved ones has changed since the confinements?<br/>4.4 When you (or your spouse) were teleworking, what did you do to present a professional image to the camera?<br/>4.5 When you (or your partner) teleworked, what did you do to avoid noise?<br/>4.6 how do you feel about telecommuting now, having lived through the pandemic?<br/>4.7 Generally speaking, how important is the Internet in your life? Has the pandemic changed anything?</td></tr></tbody></table>

(Continued)
<figure><img src="image_33.png" /><figcaption>Table B1. (Continued).</figcaption></figure>

<table><thead><tr><th>Section</th><th>Interview questions from our survey instrument</th></tr></thead><tbody><tr><td>5. Concept of home and home dimensions: participants used a 1 to 10 Likert scale to score each dimension and its period.</td><td>5.1 'At home, I feel that I (and my family) are safe'. During periods of confinement? Now? Why or why not?<br>5.2 "At home, I have all the privacy I (and my family) need. During periods of confinement? Now? Why or why not?<br>5.3 'At home, I can thrive in my relationships with the people I love' During periods of confinement? Now? Why or why not?<br>5.4 'At home, me and my family have enough space for all our activities' During periods of confinement? Now? Why or why not?<br>5.5 'At home, me and my family have the freedom to rearrange our home as we wish' During periods of confinement? Now? Why or why not?<br>5.6 'At home, I live in a place that suits the way I work, either telecommuting or on site' During periods of confinement? Now? Why or why not?<br>5.7 'At home, I and my family are protected from the pressures of the outside world' During periods of confinement? Now? Why or why not?<br>5.8 'At home, I and my family can assert our identity and lifestyles' During periods of confinement? Now? Why or why not?<br>5.9 In what environment did you grow up?<br>5.10 Do you intend to move within the next 5 years? Why or why not?<br>5.11 What type of environment would you like to live in, type of building or neighborhood?<br>5.12 With which of these two sentences do you identify the most: First sentence: I think the negative effects of events such as a pandemic can be reduced, controlled by people. Second sentence: Apart from minor modifications, I don't think there's much we can do about events like pandemics that are beyond our control. Second comparison, first sentence: when I make plans, I'm almost certain I'll be able to carry them out. Second sentence: it's not always wise to plan too far ahead, as many things turn out to be a matter of luck or misfortune.<br>5.13 Are there any advantages and disadvantages of your home during the pandemic that you'd like to discuss before ending this interview?</td></tr></tbody></table>

## Appendix C. Variable contributions to MFA dimensions

<table><caption>Table C1. Variance explained by MFA groups formed with residential situation variables and their influence on components or dimension.</caption><thead><tr><th></th><th>Dimension 1<br>(Eigenvalue = 1.54)</th><th>Dimension 2<br>(Eigenvalue = 1.32)</th><th>Dimension 3<br>(Eigenvalue = 0.99)</th><th>Dimension 4<br>(Eigenvalue = 0.94)</th></tr></thead><tbody><tr><td>MFA Variable grouping 1: age and dwelling<br>occupation density</td><td>3,06</td><td><strong>53,04</strong></td><td><strong>62,16</strong></td><td>5,49</td></tr><tr><td>MFA Variable grouping 2: Occupation profile variables</td><td><strong>25,19</strong></td><td><strong>23,77</strong></td><td><strong>23,38</strong></td><td><strong>58,00</strong></td></tr><tr><td>MFA Variable grouping 3: individual and household<br>characteristics variables</td><td><strong>43,12</strong></td><td>18,84</td><td>13,57</td><td>10,59</td></tr><tr><td>MFA Variable grouping 4: housing attributes variables</td><td><strong>28,63</strong></td><td>4,35</td><td>0,89</td><td><strong>25,92</strong></td></tr><tr><td>Total</td><td><strong>100%</strong></td><td><strong>100%</strong></td><td><strong>100%</strong></td><td><strong>100%</strong></td></tr></tbody></table>

Considered low values are below 20, medium ones range from 21 to 39, and high ones from 40+.

Reference: Kaiser (1960).
<figure><img src="image_34.png" /><figcaption>Table C2. Variable contributions to dimensions MFA.</figcaption></figure>

<table><thead><tr><th></th><th>Variable</th><th>Dim.1</th><th>Dim.2</th><th>Dim.3</th><th>Dim.4</th></tr></thead><tbody><tr><td rowspan="2">Variable Grouping 1</td><td>Age (continuous variable)</td><td>99,98</td><td>72,02</td><td>21,28</td><td>45,64</td></tr><tr><td>Dwelling occupation density (continuous variable)</td><td>0,02<br>100%</td><td>27,98<br>100%</td><td>78,72<br>100%</td><td>54,36<br>100%</td></tr><tr><td rowspan="10">Variable Grouping 2</td><td>Gross Household Income (yearly) 0-29,999</td><td>6,22</td><td>6,30</td><td>5,01</td><td>1,39</td></tr><tr><td>Gross Household Income (yearly) 30000-49,999</td><td>5,89</td><td>1,33</td><td>1,78</td><td>0,26</td></tr><tr><td>Gross Household Income (yearly) 50000-79,999.</td><td>3,88</td><td>0,06</td><td>1,69</td><td>18,22</td></tr><tr><td>Gross Household Income (yearly) 80000-99,999</td><td>0,01</td><td>0,47</td><td>9,31</td><td>4,94</td></tr><tr><td>Gross Household Income (yearly) 100,000+</td><td>7,06</td><td>1,65</td><td>5,30</td><td>1,53</td></tr><tr><td>Employment status: full-time salary</td><td>0,00</td><td>6,61</td><td>0,71</td><td>4,37</td></tr><tr><td>Employment status: part-time salary worker</td><td>0,91</td><td>0,13</td><td>0,54</td><td>0,02</td></tr><tr><td>Employment status: self-employed</td><td>0,71</td><td>24,39</td><td>1,00</td><td>19,98</td></tr><tr><td>Work modality: full-time telework</td><td>0,48</td><td>0,43</td><td>1,07</td><td>0,11</td></tr><tr><td>Work modality: hybrid work</td><td>0,77</td><td>0,06</td><td>5,59</td><td>1,48</td></tr><tr><td>Work modality: on-site</td><td>0,00</td><td>0,28</td><td>9,61</td><td>1,86</td></tr><tr><td rowspan="8">Variable Grouping 3</td><td>Student: yes</td><td>0,04</td><td>7,88</td><td>17,90</td><td>6,38</td></tr><tr><td>Student: no</td><td>0,00</td><td>1,01</td><td>2,29</td><td>0,82</td></tr><tr><td>Number of children under 9 years of age: 0</td><td>0,99</td><td>19,23</td><td>6,06</td><td>0,27</td></tr><tr><td>Number of children under 9 years of age: 1</td><td>0,12</td><td>1,12</td><td>5,98</td><td>0,22</td></tr><tr><td>Number of children under 9 years of age: 2</td><td>1,85</td><td>5,19</td><td>3,60</td><td>4,44</td></tr><tr><td>Number of children under 9 years of age: 3</td><td>0,10</td><td>8,04</td><td>8,26</td><td>2,39</td></tr><tr><td>Number of children under 9 years of age: 4</td><td>0,13</td><td>4,03</td><td>2,46</td><td>1,11</td></tr><tr><td>Couple Status: couples living together</td><td>3,38</td><td>0,01</td><td>0,90</td><td>0,08</td></tr><tr><td>Couple Status: couples living apart</td><td>8,75</td><td>0,75</td><td>2,33</td><td>0,66</td></tr><tr><td>Couple Status: single mother</td><td>9,28</td><td>0,20</td><td>2,47</td><td>2,02</td></tr><tr><td>Family Type: Not a recomposed family</td><td>4,62</td><td>0,36</td><td>0,88</td><td>0,00</td></tr><tr><td>Family Type: Recomposed family</td><td>15,26</td><td>1,18</td><td>2,92</td><td>0,01</td></tr><tr><td rowspan="8">Variable Grouping 4</td><td>Neighborhood: rural</td><td>0,11</td><td>0,30</td><td>0,76</td><td>3,96</td></tr><tr><td>Neighborhood: urban</td><td>0,02</td><td>0,06</td><td>0,15</td><td>0,76</td></tr><tr><td>Housing Type: single-family home</td><td>1,86</td><td>0,60</td><td>0,03</td><td>2,03</td></tr><tr><td>Housing Type: condominium apartment</td><td>0,01</td><td>0,02</td><td>1,26</td><td>1,49</td></tr><tr><td>Housing Type: loft-studio apartment</td><td>12,91</td><td>3,89</td><td>0,07</td><td>9,00</td></tr><tr><td>Tenure Type: homeowner</td><td>1,71</td><td>0,52</td><td>0,01</td><td>1,19</td></tr><tr><td>Tenure type: renter</td><td>12,91</td><td>3,89</td><td>0,07</td><td>9,00</td></tr><tr><td></td><td>100%</td><td>100%</td><td>100%</td><td>100%</td></tr></tbody></table>

## Quantitative explorations of Home scores

<table><caption>Table C3. Correlations between the sum of home dimension scores (during the pandemic and during the interview) and mental health indices (DASS21 and WEMWBS).</caption><thead><tr><th></th><th></th><th>Well-being</th><th>Depression</th><th>Anxiety</th><th>Stress</th></tr></thead><tbody><tr><td rowspan="3">Sum of all Home scores during the interview</td><td>Correlation coefficient</td><td><strong>-,352*</strong></td><td>-0,285</td><td><strong>-,362*</strong></td><td><strong>-,366*</strong></td></tr><tr><td>Sig.</td><td>0,048</td><td>0,113</td><td>0,046</td><td>0,043</td></tr><tr><td>N</td><td>32</td><td>32</td><td>31</td><td>31</td></tr><tr><td rowspan="3">Sum of all Home scores during the pandemic</td><td>Correlation coefficient</td><td><strong>-,426*</strong></td><td>-0,331</td><td>-0,114</td><td>-0,307</td></tr><tr><td>Sig.</td><td>0,015</td><td>0,064</td><td>0,540</td><td>0,093</td></tr><tr><td>N</td><td>32</td><td>32</td><td>31</td><td>31</td></tr><tr><td rowspan="3">Home dimension scores difference</td><td>Correlation coefficient</td><td>0,195</td><td>0,192</td><td>-0,045</td><td>0,242</td></tr><tr><td>Sig.</td><td>0,284</td><td>0,292</td><td>0,810</td><td>0,190</td></tr><tr><td>N</td><td>32</td><td>32</td><td>31</td><td>31</td></tr></tbody></table>

<footnote>*p< 0.05.</footnote>
<figure><img src="image_35.png" /></figure>

## MFA groups of variables defining the residential situation

<table><caption>Table C4. MFA groups of variables defining the residential situation.</caption><thead><tr><th>Groups</th><th>Variables</th></tr></thead><tbody><tr><td>Variable grouping 1 (continuous variables)</td><td>Age</td></tr><tr><td>Variable grouping 2 (categorical variables)</td><td>Dwelling occupation density<br>Income: gross household income<br>Employment status<br>Work modality<br>Student status</td></tr><tr><td>Variable grouping 3 (categorical variables)</td><td>Number of children under 9 years of age<br>Couple status<br>Family type, recomposed family or not</td></tr><tr><td>Variable grouping 4 (categorical variables)</td><td>Neighborhood type<br>Housing type (apartments or single-family homes)<br>Tenure type</td></tr><tr><td>Supplementary variable, comprising combinations of stress and anxiety scores</td><td>Eight stress and anxiety combinations</td></tr></tbody></table>

## Residential situation profiles identified by the MFA for both samples

The following table shows the number of participants belonging to each residential situation profile in both samples:

<table><caption>Table C5. Participants belonging to each of the four residential situation profiles identified by the MFA for the sample of 538 participants and the sample of 33 interview participants.</caption><thead><tr><th>N = 538</th><th>Frequency</th><th>%</th></tr></thead><tbody><tr><td>Residential situation profile 1</td><td>162</td><td>30,1</td></tr><tr><td>Residential situation profile 2</td><td>153</td><td>28,4</td></tr><tr><td>Residential situation profile 3</td><td>108</td><td>20,1</td></tr><tr><td>Residential situation profile 4</td><td>115</td><td>21,4</td></tr><tr><td>Total</td><td>538</td><td>100</td></tr></tbody><thead><tr><th>N = 33</th><th>Frequency</th><th>%</th></tr></thead><tbody><tr><td>Residential situation profile 1</td><td>4</td><td>12%</td></tr><tr><td>Residential situation profile 2</td><td>7</td><td>21%</td></tr><tr><td>Residential situation profile 3</td><td>9</td><td>27%</td></tr><tr><td>Residential situation profile 4</td><td>13</td><td>39%</td></tr><tr><td>Total</td><td>33</td><td>100%</td></tr></tbody></table>
<figure><img src="image_36.png" /></figure>

## Appendix D. Adaptations for discussion

<table><caption>Table D1. Residential situations and their adaptations.</caption><thead><tr><th>Common Adaptations</th><th>Profile 1</th><th>Profile 2</th><th>Profile 3</th><th>Profile 4</th></tr></thead><tbody><tr><td>Material</td><td>Creating Dedicated Workspaces: Adapting various areas into makeshift offices or workspaces.<br>Acquiring Furniture and Equipment: Purchasing desks, ergonomic chairs, and digital devices for teleworking and online education.<br>Spatial Reorganization: Reconfiguring living spaces to accommodate work, study, and leisure activities.</td><td></td><td></td><td></td></tr><tr><td>Time</td><td>Adjusting Work Schedules: Aligning work hours with family needs or children's routines.<br>Establishing Routines: Creating structured daily routines for work, childcare, and education.<br>Balancing Work and Childcare: Actively participating in children's education and managing work-life balance.</td><td></td><td></td><td></td></tr><tr><td>Cognitive</td><td>Reconceptualizing Home: Shifting perception of home from a purely private space to a multifunctional environment.<br>Altered Views on Work and Technology: Adjusting attitudes toward the role of work and the use of technology in daily life.<br>Awareness of Spatial Needs: Recognizing the importance of personal space and the impact of living conditions on mental health.</td><td></td><td></td><td></td></tr><tr><td>Unique adaptations</td><td></td><td></td><td></td><td></td></tr><tr><td>Material</td><td>High reliance on technology for space management, use of communal spaces.</td><td>Conversion of home areas into multifunctional spaces, emphasis on technological integration.</td><td>Creation of specific work and study areas, investment in noise-canceling devices.</td><td>Adapting smaller spaces for work, seeking external spaces for relaxation.</td></tr><tr><td>Time</td><td>Flexible scheduling to allocate individual time and space within limited living areas.</td><td>Creative engagement of children in educational activities, establishment of professional appearance during remote work.</td><td>Delegation between partners for childcare, scheduling personal time for relaxation.</td><td>Establishing strict work hours and personal boundaries.</td></tr><tr><td>Cognitive</td><td>Heightened concern for personal privacy and space optimization due to confined living conditions.</td><td>Valuation of outdoor spaces for recreation, reflection on the dual roles of work and parenting.</td><td>Increased focus on physical and mental health management.</td><td>Recognition of the limitations of apartment living, desire for more open environments.</td></tr></tbody></table>