# Understanding depression and emotion regulation in jobseekers: an expectancy-value perspective

Julien Maestracci Torre, Jean-Baptiste Pavani, Bruno Dauvier

► To cite this version:

Julien Maestracci Torre, Jean-Baptiste Pavani, Bruno Dauvier. Understanding depression and emotion regulation in jobseekers: an expectancy-value perspective. Personality and Individual Differences, 2025, 246, pp.113359. 10.1016/j.paid.2025.113359. hal-05464433

HAL Id: hal-05464433

https://hal.science/hal-05464433v1

Submitted on 19 Jan 2026

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
Abstract

Individual differences in depression among jobseekers considerably influence the way they regulate their emotions during the job search process. Expectancy-value models of emotion regulation suggest that part of this influence is explained by the utility value individuals place on different emotion regulation strategies, as well as their perceived self-efficacy in using these strategies. The present study aimed to test these hypotheses. Across two cross-sectional studies, each involving 313 jobseekers, participants were assessed on their level of depression, their use of three distinct emotion regulation strategies during their job search (i.e., problem-solving, cognitive reappraisal, rumination), the utility value they assigned to each strategy, and their self-efficacy in employing them. Two main findings emerged. First, depression was associated with increased rumination during the job search, decreased cognitive reappraisal, and showed inconsistent associations with problem-solving. Second, the utility value assigned to a given strategy and self-efficacy in using it generally mediated the effect of depression on the use of that strategy. These findings provide insight into how individual differences in depression influence emotion regulation strategy use through motivation-related mechanisms.

**Keywords:** emotion regulation, depression, self-efficacy, utility value, jobseekers
Introduction

Unemployment renders certain individuals more susceptible to a range of health problems,
including mental health disorders (Cygan-Rehm et al., 2017; Paul & Moser, 2009; Vinokur &
Price, 2015; Virgolino et al., 2022). In particular, unemployment has been shown to increase
individuals' depressive symptoms (Amiri, 2021; Jefferis et al., 2011). Importantly, depressive
symptoms represent a painful experience for individuals. It is, for instance, one of the highest
risk factors for suicide (Brådvik, 2018). In the context of unemployment, depressive
symptoms have also been shown to lower individuals' likelihood of being re-employed
(Skärlund et al. 2012; Wanberg, 2011; Wege et al., 2017).

Thus, understanding the main mechanisms responsible for the deleterious effect of
depression on re-employment is an important goal to reach. According to certain
theoreticians, these mechanisms consist of ineffective emotion regulation processes (Gross,
2015; Park et al., 2019; Vanderlind et al., 2020). More specifically, in individuals with
elevated depression, they consist of the use of maladaptive *emotion regulation strategies* (i.e.,
cognitive or behavioral processes such as distraction, positive thinking, or problem-solving
through which individuals attempt to experience the emotions they wish to experience; Gross,
2015; Park et al., 2019; Vanderlind et al., 2020). As compared with less depressed individuals,
individuals with elevated depressive symptoms are shown to more rarely enact adaptive
emotion regulation strategies such as *problem solving* (i.e., taking direct actions to change the
situation responsible for one's negative emotions) and *cognitive reappraisal* (i.e., changing
one's point of view about the situation responsible for one's negative emotions; Aldao et al.,
2009; Beck, 2020; Garnefski & Kraaij, 2006). They are also shown to more frequently use
maladaptive emotion regulation strategies such as *rumination* (i.e., thinking repetitively and
passively about past negative events; Joormann & Stanton, 2016; Liu & Thompson, 2017).
Albeit rarely examined in the context of unemployment, the above-mentioned individual
differences in emotion regulation strategy choices according to depression are sometimes
observed in jobseekers (Kim, 2020; Pavani et al., 2021; Wang & Yan, 2018). In other words,
when experiencing intense distress during their job search, jobseekers with elevated
depression seem to be particularly inclined to rehash past negative events (i.e., ruminate),
rather than attempting to reframe the situation (i.e., cognitive reappraisal) and continuing their
job-search efforts (i.e., problem solving). However, little is known about the reasons why
depressed jobseekers show such emotion regulation strategy choices. Indeed, very few studies
have examined the motivational factors that might explain (i.e., mediate) why job seekers
with different levels of depression use distinct emotion regulation strategies. This is
particularly surprising given that these individuals generally share common goals, such as
improving their overall well-being and alleviating their distress (Eldesouky & English, 2018).

The present study was designed to address this issue. Drawing on expectancy-value
models of decision making (Feather, 1992; Eccles & Wigfield, 2023; Tamir et al., 2014;
Vancouver et al., 2000), we hypothesized that (1) the utility value of using an emotion
regulation strategy and (2) the self-efficacy in using an emotion regulation strategy mediate a
part of the relationship between depression and the use of problem solving, cognitive
reappraisal, and rumination in jobseekers.

The effect of depression on emotion regulation strategy use

Depression refers to a cluster of intercorrelated cognitive, affective, and behavioral
characteristics, including but not limited to concentration difficulties, tiredness, sadness,
anhedonia, and hopelessness (American Psychiatric Association, 2013; Beck et al., 1996;
Vanderlind et al., 2020). Although individual differences in depression can be dichotomized
(i.e., individuals with vs. without a diagnostic of major depressive disorder), individual
differences in depression are actually distributed along a severity continuum (Beck et al., 1996; Bowins, 2014).

Because of the painful nature of depression for individuals, there are a plethora of models attempting to explain their maintenance. Emotion regulation plays a central role in these models (Aldao, 2013; Fernandez et al., 2016; Sloan et al., 2015). In these models, individuals with elevated depression are thought to be individuals who encounter durable difficulties in regulating their emotions (Nolen-Hoeksema et al., 2008). Emotion regulation can be defined as the whole set of processes involved in individuals' attempts to feel the emotions they want to feel (Gross, 2015; Pavani & Colombo, 2023; Webb et al., 2012). These mechanisms mainly involve the use of emotion regulation strategies (Livingstone & Srivastava, 2012; Naragon-Gainey et al., 2017).

Supporting these ideas, it has been reported that individuals with elevated depression tend to overuse maladaptive emotion regulation strategies such as worry and rumination (Aldao et al., 2009; Augustine & Hemenover, 2008; Joormann & Stanton, 2016; Kohl et al., 2012; Liu & Thompson, 2017; Visted et al., 2018). Albeit to a lesser extent, individuals with elevated depression also tend to underuse certain adaptive emotion regulation strategies (Aldao et al., 2009; Visted et al., 2018). These findings raise the following question: *Why do these individuals use the emotion regulation strategies that are the most likely to maintain their painful state, and avoid using the emotion regulation strategies that are the most likely to change this state?*

Initial studies attempting to explain why individuals with elevated depression tend to use maladaptive emotion regulation strategies were conducted by Papageorgiou and Wells (2001) and Watkins and Moulds (2005). These studies converge to suggest that, despite its actual positive association with depression, rumination is the subject of mixed beliefs by individuals with elevated depression. They inaccurately consider it an effective manner to downregulate
negative emotions. Such initial studies are interesting because they invite us to think about
emotion regulation within an expectancy-value framework: individuals may choose emotion
regulation strategies based on their perceived *expected utility* (i.e., the extent to which they
believe a given strategy is likely to be effective for them).

The mediation effect of expectancy-value in the depression-emotion regulation
strategy use relationship

Expectancy-value models aim to explain why individuals perform the behaviors they
perform (Eccles & Wigfield, 2023; Tamir et al., 2014; Vancouver & Purl, 2016). According to
these models, behaviors are determined by two variables: the expectancy and value attributed
to the behavior (Eccles & Wigfield, 2023; Tamir et al., 2014; Vancouver & Purl, 2016).
Expectancy-value models are particularly well suited to the study of emotion regulation, as
the decision to use a specific strategy may depend on both the individual's expectancy and the
value they attribute to it (Tamir et al., 2014; Sheppes et al., 2012).

In the most exhaustive expectancy-value models, expectancy consists of two sub-
constructs: (1) self-efficacy in successfully completing a task and (2) outcome expectations.
In these models, value is also multidimensional, comprising four subconstructs: (1) intrinsic
value, or the anticipated pleasure one expects to derive from performing the task; (2) utility
value, or how well a specific task is effective in achieving a given goal; (3) attainment value,
or the importance, in terms of one's identity, of engaging in a specific task; and (4) cost, or the
set of negative consequences of performing the task. To streamline our model, we have
chosen to focus on a single dimension of expectancy—self-efficacy—and a single
subconstruct of value, namely utility value.

Self-efficacy is defined as the judgment that a person makes about his or her own abilities
to organize and carry out activities that enable him or her to achieve certain types of results
(Bandura, 1997). It can be exemplified by the following sentence: "*Am I able to do this?*".
Some individuals perceive themselves as less capable of effectively regulating their emotions than others (Caprara et al., 2008; Gratz & Roemer, 2003), which leads to a reduced use of adaptive emotion regulation strategies, such as cognitive reappraisal (Sörman et al., 2021). Higher levels of depression have precisely been shown to increase the likelihood of individuals exhibiting low general self-efficacy, and more specifically, low self-efficacy in effectively regulating their emotions (Jones et al., 2023; Milanovic et al., 2018; Rezayat & Dehgannayeri, 2013; Sörman et al., 2021).

The utility value of a behavior refers to the extent to which a specific behavior is perceived as effective in achieving a given goal (Eccles & Wigfield, 2023). It can be framed as the following question: "Does this behavior bring me closer to my goal, push me further away from it, or is it irrelevant to my goal?" In the context of emotion regulation, the utility value of a behavior (i.e., an emotion regulation strategy) reflects the degree to which an individual believes it facilitates the attainment of their emotional goals, which generally involve high levels of well-being (Pavani et al., 2025). Importantly, research has shown that depression tends to increase the perceived utility of maladaptive emotion regulation strategies such as rumination (Alfaraj et al., 2009; Cano-López et al., 2021; Kubiak et al., 2013; Moulds et al., 2010; Papageorgiou & Wells, 2001; Takano et al., 2019). Similarly, some findings suggest that higher levels of depression are associated with a decreased utility value assigned to adaptive emotion regulation strategies, such as problem-solving and cognitive reappraisal (Miner et al., 2015).

## The Present Study

As mentioned above, few studies have examined the motivational factors (e.g., expectancy and value) that explain why job seekers with varying levels of depression use different emotion regulation strategies during their job search. Furthermore, to our knowledge, none of these studies have proposed mediation models to explore this issue.
The present research was designed to address this gap. It comprises two cross-sectional studies in which jobseekers' depression was assessed, as well as one component of expectancy (i.e., self-efficacy in using an emotion regulation strategy) and one component of value (i.e., the perceived utility of using a strategy). These factors were analyzed in relation to three emotion regulation strategies that are thought to be influenced by depression: rumination, cognitive reappraisal, and problem-solving (i.e., job search efforts). Two separate studies were conducted, considering the importance and urgency of addressing the replicability crisis in psychological science (Anvari & Lakens, 2018).

Two main pre-registered mediation hypotheses were formulated. First, among jobseekers, the self-efficacy in the use of an emotion regulation strategy to regulate the emotions elicited by negative events during job search would mediate the effect of depression on the use of the strategy of interest. Second, among these individuals, the utility value of using an emotion regulation strategy to regulate the emotions elicited by negative events during job search would mediate the effect of depression on the use of the strategy of interest. In other words, more depression would be accompanied by more use of rumination, partly because depression is accompanied by the belief that using these strategies is more useful and feasible. Similarly, more depression would be accompanied by less use of cognitive reappraisal and problem solving, partly because depression is accompanied by the belief that using these strategies is less useful and feasible.

# Method

## Transparency and openness

We describe our sampling plan, all data exclusions (if any), all manipulations, and all measures in the study, and we adhered to the *Journal of Applied Psychology* methodological checklist. All data, analysis code, and research materials are available at [https://osf.io/y35mg/?view_only=c3436720edb642588a73fbded9f92454](https://osf.io/y35mg/?view_only=c3436720edb642588a73fbded9f92454). Data were
analyzed using R, version 4.0.0 (R Core Team, 2024) and the package *ggplot*, version 3.2.1 (Wickham, 2016). The hypotheses and analysis were preregistered [https://aspredicted.org/r3br-zxms.pdf].

## Participants

The Sample size was determined with a power analysis, computed using the mc-power-
med application (Schoemann et al., 2017). For a mediation composed of (1) a weak-to-
moderate effect of the supposed predictor on the supposed mediator (i.e., B = 0.225), and (2) a
weak-to-moderate effect of the supposed mediator on the supposed outcome (i.e., B = 0.225),
and with a significance level set at 0.05, a sample size of at least 296 individuals was required
to achieve 95% power<fnref n="1" />. To obtain such a sample size, the recruitment method that we used
was *convenience sampling*, for its recognized efficiency (Etikan et al., 2016). Two inclusion
criteria were applied, namely being at least 18 years old and being actively seeking
employment.

Recruitment was primarily conducted through social media platforms, specifically
Facebook and LinkedIn. We targeted private groups dedicated to job searching, which are not
professional networks but rather self-managed communities created by job seekers to support
one another (e.g., sharing job postings, providing tips on cover letter writing, organizing
online resume workshops). These groups were selected based on their level of activity and
their relevance to our research objectives. To gain access, we sought permission from the
group administrators, explaining that we were psychology researchers conducting a study on
job seekers and mental health. Once approval was granted, we posted an announcement
introducing our research team, outlining the study's objectives, and providing a hyperlink to

<footnote n="1">¹ In this power analysis, we chose to set the direct effect of the supposed predictor on the supposed outcome at 0.09, in order for it to be identical to the indirect effects (i.e., 0.30 * 0.30 = 0.09). Moreover, we chose a significance level at 0.01 rather than 0.05 because we planned to perform several mediation analyses (i.e., one for each outcome variable).</footnote>
the questionnaire. Participation in the experiment was voluntary and did not involve any
financial compensation.

With this method, we recruited 313 French adults for Study 1 (84% female, 15% male, 1% other), aged 18-65 years (M = 33.9, SD = 12.1) and 313 French adults for Study 2 (83% female, 16% male, 1% other), aged 18-65 years (M = 35.5, SD = 12.2). The majority have been seeking employment for less than six months (for Study 1: 64% have been seeking employment for less than six months and 36% have been seeking employment for more than six months; for Study 2: 55% have been seeking employment for less than six months and 45% have been seeking employment for more than six months). Also, the vast majority have previously looked for a job before (for Study 1: 81% have previously looked for a job before and 19% have never looked for a job before; for Study 2: 83% have previously looked for a job before and 17% have never looked for a job before). Lastly, the number of participants who had already been diagnosed with major depressive disorder by a psychiatrist varied from study to study (for Study 1: 44% had already been diagnosed with major depressive disorder and 56% had never been diagnosed with major depressive disorder; for Study 2: 61% had already been diagnosed with major depressive disorder and 39% had never been diagnosed with major depressive disorder).

## Procedure

The procedure was in accordance with the 1964 Declaration of Helsinki and its later amendments. All participants explicitly gave their informed consent. Participants were informed that they were free to stop the experiment at any time without providing any justification. They were also informed that they had the right to request that their results be removed from our database.

The procedure was identical in both studies. It consisted in filling out a single online questionnaire battery. Participants were first asked for sociodemographic information (e.g.,
gender, age, job search duration, past job search experiences, major depressive disorder
diagnosis). They were then invited to fill in several questionnaires assessing psychological
variables. The variables evaluated were depression, emotion regulation strategies generally
enacted in reaction to negative events related to job search, the utility value of using these
emotion regulation strategies, and the perceived self-efficacy in enacting these emotion
regulation strategies.

Materials

Depression

Depression was assessed using the French validation of the *Beck Depression Inventory II* (BDI-II; Bouvard & Cottraux, 2019). The BDI-II is a self-report questionnaire for measuring the intensity of depression. It was designed to assess the symptomatology defined by the DSM-5 (American Psychiatric Association, 2013). Participants were asked to choose one answer from four options varying in intensity on twenty-one major themes. Participants were asked to respond to items based on their experience over the past two weeks. For example, item 12 concerns loss of interest and invites the participant to choose between the following four options: "I have not lost interest in people or activities"; "I am less interested in people and things than before"; "I am hardly interested in people and things anymore"; and "I find it hard to be interested in anything". Each item yields scores ranging from 0 (the least intense option) to 3 (the most intense option). Averaging scores for the 21 issues yielded the indicator of depression intensity that we used in this study (for Study 1: μ = 0.90; for Study 2: μ = 0.98). Internal consistency was good (for Study 1: α = 0.92; for Study 2: α = 0.9).

Emotion regulation strategy use

Emotion regulation strategy use during job search was evaluated for three strategies:
problem solving, cognitive reappraisal and rumination. To assess the use of problem solving,
we adapted three items assessing this strategy in the French validation of the *Brief COPE*
(Muller & Spitz, 2004) for these items to target job-search efforts. To assess the use of cognitive reappraisal and rumination, we extracted three items designed to assess each of these strategies from the French validation of the Cognitive Emotion Regulation Questionnaire (Jermann et al., 2006)<fnref n="2" />. Examples of items were "I redoubled my efforts in my job search for problem solving, I thought I could learn something from the situation" for cognitive reappraisal and "I've thought a lot about how I felt regarding what I experienced for rumination". The instruction of this 9-item questionnaire invited participants to indicate what they generally do in reaction to the negative emotion-eliciting events that they encounter during their job search. The Likert scale ranged from 0 ("Not at all") to 4 ("Always").

As these are new assessment tools, their validity was estimated in a pre-test conducted with 100 job-seeking participants (see osf files). This pre-test provided several clues in favor of the good validity of these items, and in particular, of their factor structure: a 3-factor model (one factor for the problem-solving strategy, one factor for the cognitive reappraisal strategy and one factor for the rumination strategy) fit pretest data well<fnref n="3" />. This acceptable factor structure was obtained again in our main studies (for Study 1: Robust CFI = 0.98; Robust TLI = 0.97; Robust RMSEA = 0.045; SRMR = 0.051; for Study2: Robust CFI = 0.98; Robust TLI = 0.97; Robust RMSEA = 0.042; SRMR = 0.042)

In the two present studies, internal consistency was good (for Study 1: α for problem solving strategy = 0.88; α for cognitive reappraisal strategy = 0.72; α for rumination strategy = 0.76; for Study 2: α for problem solving strategy = 0.85; α for cognitive reappraisal strategy = 0.78; α for rumination strategy = 0.74).

<footnote n="2">² Initially, four items per strategy were used. However, a pretest study conducted among 100 participants suggested that the validity of our questionnaire was improved by retaining only three items per strategy (see the pretest-related OSF file).</footnote>

<footnote n="3">³ In the pretest, both exploratory and confirmatory factor analyses were conducted. Whether for the items used in the main studies to assess strategy use, self-efficacy in the use of strategies, or the utility value of strategies, the exploratory factor analyses suggested that the three-factor model (problem solving, cognitive reappraisal, rumination) was the best fit. Similarly, the confirmatory factor analyses consistently indicated that the three-factor model provided an acceptable fit to the data.</footnote>
## Self-efficacy in using an emotion regulation strategy

Self-efficacy in using problem solving, cognitive reappraisal and rumination during job search were assessed using another questionnaire containing the same 9 items as above. However, the instruction was changed, inviting participants to indicate how much they felt they would have been able to use these strategies if they had decided to. Their responses were registered using visual analogue scale that yielded scores ranging from 0 ("Totally unable") to 100 ("Totally able").

As these are new assessment tools, their validity was estimated in a pre-test conducted with 100 job-seeking participants (see osf files). This pre-test provided several clues in favor of the good validity of these items, and of their factor structure: a 3-factor model (one factor for the problem-solving strategy, one factor for the cognitive reappraisal strategy and one factor for the rumination strategy) fit the data well. (for Study 1: Robust CFI = 0.96; Robust TLI = 0.94; Robust RMSEA = 0.082; SRMR = 0.077; for Study2: Robust CFI = 0.96; Robust TLI = 0.95; Robust RMSEA = 0.094; SRMR = 0.070)

In the two present studies, internal consistency was good (for Study 1: α for self-efficacy of using problem solving strategy = 0.92; α for self-efficacy of using cognitive reappraisal strategy = 0.85; α for self-efficacy of using rumination strategy = 0.71; for Study 2: α for self-efficacy of using problem solving strategy = 0.94; α for self-efficacy of using cognitive reappraisal strategy = 0.91; α for self-efficacy of using rumination strategy = 0.82).

## Utility value of using an emotion regulation strategy

The utility value of using problem solving, cognitive reappraisal and rumination during job search were assessed using once again using the above-mentioned 9 items. However, the instruction was changed, inviting participants to indicate how much they felt they would have found it useful to use each emotion regulation strategy. Their responses were registered using
visual analogue scale that yielded scores ranging from 0 (*Totally useless*) to 100 (totally useful).

As these are new assessment tools, their validity was estimated in a pre-test conducted with 100 job-seeking participants (see osf files). This pre-test provided several clues in favor of the good validity of these items, and of their factor structure: a 3-factor model (one factor for the problem-solving strategy, one factor for the cognitive reappraisal strategy and one factor for the rumination strategy) fit the data well. (for Study 1: Robust CFI = 0.97; Robust TLI = 0.96; Robust RMSEA = 0.066; SRMR = 0.045; for Study2: Robust CFI = 0.96; Robust TLI = 0.95; Robust RMSEA = 0.074; SRMR = 0.053)

In the two present studies, internal consistency was good (for Study 1: α for utility value of using problem solving strategy = 0.92; α for utility value of using cognitive reappraisal strategy = 0.84; α for utility value of using rumination strategy = 0.71; for Study 2: α for utility value of using problem solving strategy = 0.90; α for utility value of using cognitive reappraisal strategy = 0.86; α for utility value of using rumination strategy = 0.75).

## Data analytic strategy

Three mediation analyses were performed using the *lavaan* R package (Rosseel, 2012). Each mediation analysis was computed using 1000 bootstrap samples. The outcome variable differed in each analysis (i.e., rumination for the first analysis, cognitive reappraisal for the second analysis, problem solving for the third analysis). In each analysis, two parallel mediators were examined (i.e., the self-efficacy in using the strategy of interest and the utility value of using this strategy). Finally, in each mediation analysis, the predictor variable was depression intensity.

## Results

### Study 1

#### Descriptive statistics
Descriptive statistics and intercorrelations between our variables of interest are displayed in Table 1 and in Tables S1 to S3 (see OSF files). Three possibly-interesting findings already emerged at this descriptive level. Firstly, the utility value of using rumination appeared to be lower than the utility value of using the two other strategies, whereas the self-efficacy in using the strategies and the reported use of these strategies differed less. Secondly, the use of problem solving and cognitive reappraisal appeared to be negatively correlated with individuals' level of depression (e.g., for cognitive reappraisal; r = -0.27; p < 0.001) while the use of rumination appeared to be positively correlated with depression level (r = 0.51; p < 0.001). Thirdly, the utility value of using an emotion regulation strategy appeared to be systematically positively correlated with its use. For example, the utility value of rumination is positively correlated with its use (r = 0.49; p < 0.001). Likewise, the self-efficacy in using an emotion regulation strategy appeared to be systematically positively correlated with its use. For example, the self-efficacy in using problem solving was positively correlated with its use (r = 0.64; p < 0.001).

(Insert Table 1 approximately here.)

## Hypothesis Testing

Results of the mediation analyses that we performed are displayed in Table 2.<fnref n="4" /> The regression analyses that were required to estimate the parameters on which these mediation analyses are based are displayed in OSF files (Table S4).

(Insert Table 2 approximately here.)

<footnote n="4">⁴ The mediation analyses were also conducted with all six mediators included simultaneously (i.e., the utility value of using each of the three strategies and the self-efficacy in using each of the three strategies), and this did not alter the results. Neither the effect sizes nor their statistical significance changed. This result was also obtained in Study 2.</footnote>
Results regarding rumination and cognitive reappraisal were consistent with our hypotheses in both studies. More specifically, as predicted, depression level appeared to be positively related to the use of rumination in Study 1 (β = 0.59, p < 0.001). Furthermore, still as expected, a part of this relationship appeared to be mediated by the self-efficacy in using rumination (13.56%; β = 0.08, p < 0.001) and the utility value of using this strategy (12%; β = 0.07, p < 0.001) in both studies. In other words, as compared with less depressed individuals, individuals with high depression levels appeared to engage more in rumination in reaction to negative emotion-elicitimg events during their job search, partly because they feel more able to do so and because they think it would be more useful.

Regarding cognitive reappraisal, as predicted depression level appeared to be negatively related to the use of this strategy in Study 1 (β = -0.40, p < 0.05). Furthermore, still as expected, a part of this relationship appeared to be mediated by the self-efficacy in using cognitive reappraisal (42.5%; β = -0.17, p < 0.001) and the utility value of using this strategy (20%; β = -0.08, p < 0.001) in both studies. In other words, as compared with less depressed individuals, individuals with high depression levels appeared to engage less in cognitive reappraisal in reaction to negative emotion-elicitimg events during their job search, partly because they feel less able to do so and because they think it would be less useful.

Results regarding problem solving were less consistent with our hypotheses. More specifically, depression level appeared to be negatively related to the use of problem solving (β = -0.16, p < 0.01). A part of this relationship (i.e., 44%) was significantly mediated by the self-efficacy in using this strategy (β = -0.07, p < 0.05), but not by the utility value of using this strategy (β = -0.017, p = 0.18). In other words, as compared with less depressed individuals, individuals with high depression levels appeared to engage less in problem
solving in reaction to negative emotion-eliciting events during their job search, partly because they feel less able to do so.

# Study 2

## Descriptive statistics

Descriptive statistics and intercorrelations between our variables of interest are presented in Table 3 and in Tables S5 to S8 (see OSF files). In Study 2, the results confirmed and replicated those obtained in Study 1. First, the utility value attributed to rumination was significantly lower than that of the other two strategies, whereas differences in perceived self-efficacy and reported use of these strategies were less pronounced. Next, correlation analyses showed that the use of adaptive strategies, such as problem-solving and cognitive reappraisal, was negatively associated with depression levels (for cognitive reappraisal, *r* = -0.38, *p* < 0.001), while rumination was positively correlated with depression (*r* = 0.42, *p* < 0.001). Finally, we found that the utility value of a strategy was consistently positively associated with its actual use. For instance, the utility value of rumination was positively related to its use (*r* = 0.37, *p* < 0.001), and self-efficacy in using problem-solving showed a strong positive correlation with its use (*r* = 0.66, *p* < 0.001).

(Insert Table 3 approximately here.)

## Hypothesis Testing

The results of the mediation analyses are presented in Table 4. The regression analyses used to estimate the parameters underlying these mediation models can be found in the OSF files (Table S8).

(Insert Table 4 approximately here.)
In Study 2, the results for rumination and cognitive reappraisal replicated the patterns observed in Study 1. For rumination, depression levels remained positively associated with its use ($\beta$ = 0.53, *p* < 0.001). Moreover, this relationship was partially mediated by two mechanisms: first, the perceived efficacy of using rumination ($\beta$ = 0.08, *p* < 0.001), which accounted for approximately 15% of the mediation effect, and second, the utility value attributed to this strategy ($\beta$ = 0.04, *p* < 0.05), which explained around 8% of the effect. In other words, compared to less depressed individuals, those with higher levels of depression tend to engage in rumination more frequently in response to emotionally negative events, partly because they feel more capable of using it and perceive it as a more useful strategy.

Regarding cognitive reappraisal, Study 2 also confirms that depression levels are negatively associated with the use of this strategy ($\beta$ = -0.46, *p* < 0.001). This negative relationship is partially mediated by two factors: a decrease in perceived efficacy in using cognitive reappraisal, which accounts for approximately 41% of the mediation effect ($\beta$ = -0.19, *p* < 0.001), and a decrease in the utility value attributed to this strategy, which explains around 15% of the effect ($\beta$ = -0.05, *p* < 0.05). Thus, compared to less depressed individuals, those with higher levels of depression tend to use cognitive reappraisal less frequently when dealing with negative emotions, partly because they feel less capable of employing it and perceive it as less useful.

However, Study 2 did not replicate the findings from Study 1 regarding the problem-solving strategy. No significant relationship was found between depression and the use of this strategy ($\beta$ = 0.002, *p* = 0.08).

## Alternative models

Some theoreticians suggest that the motivation to engage in a behavior (e.g., use rumination) is imperfectly explained by the addition of the expectancy assigned to this
behavior (e.g., the self-efficacy in using rumination) and the value assigned to this
behavior (e.g., the utility value of using rumination; Vancouver & Purl, 2016). These
theoreticians propose that the motivation to engage in a behavior is better explained by
the multiplication between expectancy and value (granted that the lower possible score
on both variables is set at 0 so that expectancy and value should be both present to a
certain degree to motivate behaviors; Vancouver & Purl, 2016). To test this, for each
emotion regulation strategy, we tested and compared two regression models on their
degree of fit to the data, as assessed by the Bayesian Information Criterion (BIC). The
first regression model consisted of a model in which the use of the strategy was regressed
on two predictors (i.e., the self-efficacy in using the strategy and the utility value of using
it). The second model consisted of a model in which the use of the strategy was regressed
on a single predictor, obtained by multiplying the self-efficacy in using the strategy and
the utility value of using it after having bounded both variables between 0 and 1. These
comparisons were performed for Study 1 and 2.

Results were similar in both studies. The second (i.e., multiplicative) model displayed
a better fit to the data than the first (i.e., additive) model for cognitive reappraisal (BIC =
595 vs. 609), problem solving (BIC = 659 vs. 660), but not rumination (BIC = 609 vs.
602).

Discussion

Interpretation of results

Two main types of results were obtained. The first type regards the degree to which
jobseekers' level of depression is accompanied by the tendency to engage in maladaptive
emotion regulation strategies and disengage from adaptive emotion regulation strategies.
More specifically, consistent with previous findings suggesting that depression level is
associated with more rumination use when individuals attempt to regulate their negative
emotions (Aldao et al., 2009; Augustine & Hemenover, 2008; Joormann & Stanton, 2016; Kohl et al., 2012; Liu & Thompson, 2017; Papageorgiou & Wells, 2001; Visted et al., 2018; Webb et al., 2012), we observed in both studies that job seekers' level of depression was positively related to their engagement in rumination in reaction to negative emotion-elicitin
events during their job search. In other words, the higher the level of depression in a job seeker, the more likely they are to engage in rumination to regulate the negative emotions experienced during job search.

Previous findings also suggested that individuals with high levels of depression tend to underuse so-called *adaptive* emotion regulation strategies, such as cognitive reappraisal and problem solving (Aldao et al., 2009; Visted et al., 2018). Our research partially bears out these results. In both studies, we observed a negative relationship between job seekers' level of depression and the use of cognitive reappraisal. In other words, the higher the level of depression in a job seeker, the less likely they are to use cognitive reappraisal strategies in reaction to negative emotion-elicitin
events during job search. By contrast, only Study 1 suggested a negative relationship between jobseekers' level of depression and the use of problem solving.

The second type of results obtained in the present research regards the possible reasons
why jobseekers with a high depression level use more maladaptive emotion regulation
strategies and less adaptive emotion regulation strategies than less depressed jobseekers.
Although these variables have rarely been analyzed in the context of emotion regulation
during job search, we hypothesized that the self-efficacy in using one strategy or another and
the utility value of using these strategies could represent such reasons. We extracted those
hypotheses from research on motivation and self-regulation (Vancouver, 2000; Vancouver &
Purl, 2016) and initial studies on this topic in the context of emotion regulation (Papageorgiou
& Wells, 2001; Watkins & Moulds, 2005). These hypotheses were almost systematically
corroborated. In both studies, jobseekers with high depression levels appeared to engage more
in rumination than less depressed jobseekers partly because they feel more able to do so and
partly because they think that engaging in rumination would help them regulate their negative
emotions. Likewise, in both studies, jobseekers with high depression levels appeared to
engage less in cognitive reappraisal than less depressed jobseekers, partly because they feel
less able to do so and partly because they think that engaging in cognitive reappraisal would
help them regulate their negative emotions to a lesser extent. By contrast, results regarding
problem solving provided mixed support for our hypotheses. In the only study in which
depression level was related to problem-solving use (i.e., Study 1), self-efficacy, but not utility
value, mediated a part of this relationship.

These results may provide insight into how depressed job seekers navigate their job
search. If they engage in seemingly ineffective behaviors (i.e., behaviors that perpetuate
distress and unemployment), it is not without reason. The present study suggests that some of
these reasons can be understood. Individuals with elevated levels of depression are prone to
erroneous beliefs about the utility of certain behaviors, which are likely to impact their well-
being and their likelihood of reemployment. Regardless of the value they assign to specific
behaviors, job seekers with high levels of depression may engage in seemingly maladaptive
behaviors simply because these are the only behaviors they feel capable of performing.

Implications

These findings have implications for both research and clinical practice. First, they
open up promising avenues for psychological research. The mediation models employed in
this study suggest that expectancy and value exert a causal influence on the strategies
individuals use to regulate their emotions. However, causal effects can only be rigorously
established through experimental studies. For instance, in line with the expectancy-value
model, an experimental design could be developed in which participants are given a choice
between two strategies and subsequently asked to evaluate the extent to which their self-
efficacy and/or the perceived utility of the selected strategy influenced their decision.

From a clinical perspective, it may be beneficial to develop targeted interventions for job seekers experiencing high levels of depression. For example, psychoeducational programs could be implemented to emphasize the advantages of adaptive emotion regulation strategies and to challenge erroneous beliefs about maladaptive strategies such as rumination. Notably, therapeutic techniques designed to address such misconceptions about maladaptive emotion regulation strategies have already been successfully applied (Wells, 2010) and could serve as a valuable source of inspiration for practitioners working with job seekers suffering from elevated levels of depression. Furthermore, practitioners might draw upon the four primary sources of self-efficacy—mastery experiences, vicarious learning, persuasive feedback, and positive emotions (Bandura, 1997)—to help job seekers with high levels of depression develop greater self-efficacy in using adaptive emotion regulation strategies. For example, a simple technique involving the division of a challenging goal into a series of more manageable subgoals has been successfully incorporated into certain therapeutic approaches to foster problem-solving (Beck, 2020).

## Limitations

The aforementioned ideas should be interpreted with caution given the limitations of the present study. First, although we examined the impact of the value individuals assign to emotion regulation strategies on the use of these strategies, we only partially explored this relationship. Specifically, we focused on a single component of value, whereas it may actually comprise four distinct components (i.e., intrinsic value, attainment value, utility value, and cost; Wigfield et al., 2017). Future research could benefit from investigating these different forms of value to determine whether depression affects some more than others.
A second limitation concerns the way we addressed unemployment in the present study. Specifically, unemployment was treated as a characteristic of our sample rather than as a variable. As a result, we were unable to adopt a *person-situation interaction* approach, which is sometimes preferred in personality psychology (Jayawickreme et al., 2021). Including both job seekers and already-employed participants in future studies could help determine whether depression affects the motivation to use emotion regulation strategies differently depending on the situation.

A third limitation concerns the way we evaluated our motivation-related variables (i.e., the utility value of emotion regulation strategies and self-efficacy in using emotion regulation strategies). Although the pre-test study we conducted yielded findings supporting the psychometric quality of the questionnaires we developed, these questionnaires can still be improved. For instance, we used only three items per variable to minimize participant burden, as this did not compromise the internal consistency of these variables. However, some scholars may argue that four items are more appropriate for adequately assessing a dimension. In future studies, alternative assessment tools for our motivation-related variables could be explored to test the robustness of the present study's findings.

## Conclusion

Two main findings emerged from the present study. First, job seekers' level of depression positively predicted the use of maladaptive emotion regulation strategies and negatively predicted the use of adaptive ones. Put simply, the higher a job seeker's level of depression, the more likely they are to engage in maladaptive emotion regulation strategies such as rumination, and the less likely they are to use adaptive strategies such as cognitive reappraisal. Second, this relationship was mediated by self-efficacy and the perceived utility of employing an emotion regulation strategy. In other words, if higher levels of depression are
associated with increased rumination, it is because this behavior appears more useful and feasible as depression intensifies. Similarly, if higher levels of depression are linked to decreased use of cognitive reappraisal, it is because this strategy seems less useful and feasible to jobseekers experiencing greater depressive symptoms.

# Statements and Declarations

## Funding detail

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors

## Disclosure

The authors report that there are no competing interests to declare.

## Data transparency

Data and R Code are publicly available at :

https://osf.io/y35mg/?view_only=c3436720edb642588a73fbded9f92454

Hypothesis have been pre-registered : https://aspredicted.org/r3br-zxms.pdf

## References

Aldao, A. (2013). The future of emotion Regulation research. *Perspectives on Psychological Science*, 8(2), 155–172. https://doi.org/10.1177/1745691612459518

Aldao, A., Nolen-Hoeksema, S., & Schweizer, S. (2009). Emotion-regulation strategies across psychopathology: A meta-analytic review. *Clinical Psychology Review*, *30*(2), 217–237. https://doi.org/10.1016/j.cpr.2009.11.004

Alfaraj, A. M. I., Spada, M. M., Nikčević, A. V., Puffett, A., & Meer, S. (2009). Positive beliefs about rumination in depressed and non-depressed pregnant women: a
preliminary investigation. *Journal of Reproductive and Infant Psychology*, 27(1), 54-
60. https://doi.org/10.1080/02646830701759678

American Psychiatric Association. (2013). *Diagnostic and Statistical Manual of Mental Disorders*. https://doi.org/10.1176/appi books.9780890425596

Amiri, S. (2021). Unemployment associated with major depression disorder and depressive symptoms: a systematic review and meta-analysis. *International Journal of Occupational Safety and Ergonomics*, 28(4), 2080–2092. https://doi.org/10.1080/10803548.2021.1954793

Anvari, F., & Lakens, D. (2018). The replicability crisis and public trust in psychological science. *Comprehensive Results in Social Psychology, 3*(3), 266–286.
https://doi.org/10.1080/23743603.2019.1684822

Augustine, A. A., & Hemenover, S. H. (2008). On the relative effectiveness of affect regulation strategies: A meta-analysis. *Cognition & Emotion, 23*(6), 1181–1220. https://doi.org/10.1080/02699930802396556

Bandura, A. (1986). Social Foundations of Thought and Action : A Social Cognitive Theory. Prentice-Hall, Inc. https://ci.nii.ac.jp/naid/10010087207/

Bandura, A. (1997). *Self-efficacy: The exercise of control*. Macmillan.

Beck, A. T., Steer, R. A., & Brown, G. (1996). Beck Depression Inventory–II [Dataset]. In *PsycTESTS Dataset*. https://doi.org/10.1037/t00742-000

Beck, J. S. (2020). *Cognitive Behavior Therapy, third edition: Basics and Beyond*. Guilford Publications.

Bouvard, M., & Cottraux, J. (2019). *Protocoles et échelles d'évaluation en psychiatrie et psychologie* (5th ed.). Elsevier Masson. https://www.elsevier-masson.fr/protocoles-et-echelles-devaluation-en-psychiatrie-et-psychologie-9782294770210.html

Bowins, B. (2014). Depression: discrete or continuous? *Psychopathology*, *48*(2), 69–78. https://doi.org/10.1159/000366504

Brådvik, L. (2018). Suicide risk and mental disorders. *International Journal of Environmental Research and Public Health*, 15(9), 2028. https://doi.org/10.3390/ijerph15092028
Camus, G., & Berjot, S. (2015). Comment sont perçues les personnes au chômage au sein de la société française ? Étude de la composition du stéréotype. *Les Cahiers Internationaux De Psychologie Sociale, Numéro 105*(1), 53–81. https://doi.org/10.3917/cips.105.0053

Cano-López, J. B., García-Sancho, E., Fernández-Castilla, B., & Salguero, J. M. (2021). Empirical Evidence of the metacognitive model of rumination and Depression in clinical and nonclinical samples: A Systematic Review and Meta-Analysis. *Cognitive Therapy and Research, 46*(2), 367–392. https://doi.org/10.1007/s10608-021-10260-2

Caprara, G. V., Di Giunta, L., Eisenberg, N., Gerbino, M., Pastorelli, C., & Tramontano, C. (2008). Assessing regulatory emotional self-efficacy in three countries. *Psychological Assessment, 20*(3), 227–237. https://doi.org/10.1037/1040-3590.20.3.227

Cygan-Rehm, K., Kuehnle, D., & Oberfichtner, M. (2017). Bounding the causal effect of unemployment on mental health: Nonparametric evidence from four countries. *Health Economics, 26*(12), 1844–1861. https://doi.org/10.1002/hec.3510

Eccles, J. S., & Wigfield, A. (2023). Expectancy-value theory to situated expectancy-value theory: Reflections on the legacy of 40+ years of working together. *Motivation Science, 9*(1), 1–12. https://doi.org/10.1037/mot0000275

Eldesouky, L., & English, T. (2018). Individual differences in emotion regulation goals: Does personality predict the reasons why people regulate their emotions? *Journal of Personality, 87*(4), 750–766. https://doi.org/10.1111/jopy.12430

Etikan, I. (2016). Comparison of convenience sampling and purposive sampling. *American Journal of Theoretical and Applied Statistics, 5*(1), 1. https://doi.org/10.11648/j.ajtas.20160501.11

Feather, N. T. (1992). Expectancy-value theory and unemployment effects. *Journal of Occupational and Organizational Psychology, 65*(4), 315–330. https://doi.org/10.1111/j.2044-8325.1992.tb00508.x

Fernandez, K. C., Jazaieri, H., & Gross, J. J. (2016). Emotion Regulation: a transdiagnostic perspective on a new RDOC domain. *Cognitive Therapy and Research, 40*(3), 426–440. https://doi.org/10.1007/s10608-016-9772-2
Fielding, J. E. (1999). Public Health in the Twentieth Century: Advances and challenges. *Annual Review of Public Health*, 20(1), xiii–xxx. https://doi.org/10.1146/annurev.publhealth.20.1.0

Fohrbeck, A., Hirseland, A., & Lobato, P. R. (2014). How Benefits Recipients Perceive Themselves through the Lens of the Mass Media - Some Observations from Germany. *Sociological Research Online*, 19(4), 74–81. https://doi.org/10.5153/sro.3524

Garnefski, N., & Kraaij, V. (2006). Relationships between cognitive emotion regulation strategies and depressive symptoms: A comparative study of five specific samples. *Personality and Individual Differences*, 40(8), 1659–1669. https://doi.org/10.1016/j.paid.2005.12.009

Gratz, K. L., & Roemer, L. (2003). Multidimensional assessment of emotion regulation and dysregulation: development, factor structure, and initial validation of the difficulties in emotion regulation scale. *Journal of Psychopathology and Behavioral Assessment*, 26(1), 41–54. https://doi.org/10.1023/b:joba.0000007455.08539.94

Gross, J. J. (2015). Emotion Regulation: Current status and future Prospects. *Psychological Inquiry*, 26(1), 1–26. https://doi.org/10.1080/1047840x.2014.940781

Jayawickreme, E., Fleeson, W., Beck, E. D., Baumert, A., & Adler, J. M. (2021). Personality dynamics. *Personality Science*, 2(1), e6179.

Jefferis, B. J., Nazareth, I., Marston, L., Moreno-Kustner, B., Bellón, J. Á., Svab, I., Rotar, D., Geerlings, M. I., Xavier, M., Goncalves-Pereira, M., Vicente, B., Saldivia, S., Aluoja, A., Kalda, R., & King, M. (2011). Associations between unemployment and major depressive disorder: Evidence from an international, prospective study (the predict cohort). *Social Science & Medicine*, 73(11), 1627–1634. https://doi.org/10.1016/j.socscimed.2011.09.029

Jermann, F., Van Der Linden, M., D'Acremont, M., & Zermatten, A. (2006). Cognitive Emotion Regulation Questionnaire (CERQ). *European Journal of Psychological Assessment*, 22(2), 126–131. https://doi.org/10.1027/1015-5759.22.2.126
Jones, K., McCorkle, C., Muñoz, R. F., & Leykin, Y. (2023). Effect of demographic and clinical factors on depression self-efficacy. *Archives of Psychiatry and Psychotherapy, 25*(2), 25–29. https://doi.org/10.12740/app/156319

Joormann, J., & Stanton, C. H. (2016). Examining emotion regulation in depression: A review and future directions. *Behaviour Research and Therapy, 86*, 35–49. https://doi.org/10.1016/j.brat.2016.07.007

Kim, M. S. (2020). Psychiatric symptoms and emotion regulation strategies among the unemployed people in Korea: A latent profile analysis. *PLoS ONE, 15*(8), e0236937. https://doi.org/10.1371/journal.pone.0236937

Kohl, A., Rief, W., & Glombiewski, J. A. (2012). How effective are acceptance strategies? A meta-analytic review of experimental results. *Journal of Behavior Therapy and Experimental Psychiatry, 43*(4), 988–1001. https://doi.org/10.1016/j.jbtep.2012.03.004

Kubiak, T., Zahn, D., Siewert, K., Jonas, C., & Weber, H. (2013). Positive Beliefs about Rumination Are Associated with Ruminative Thinking and Affect in Daily Life: Evidence for a Metacognitive View on Depression. *Behavioural and Cognitive Psychotherapy, 42*(5), 568–576. https://doi.org/10.1017/s1352465813000325

Lim, A. Y., Lee, S., Jeon, Y., Yoo, R., & Jung, H. (2018). Job-Seeking stress, mental health problems, and the role of perceived social support in university graduates in Korea. *Journal of Korean Medical Science, 33*(19). https://doi.org/10.3346/jkms.2018.33.e149

Liu, D. Y., & Thompson, R. J. (2017). Selection and implementation of emotion regulation strategies in major depressive disorder: An integrative review. *Clinical Psychology Review, 57*, 183–194. https://doi.org/10.1016/j.cpr.2017.07.004

Livingstone, K. M., & Srivastava, S. (2012). Up-regulating positive emotions in everyday life: Strategies, individual differences, and associations with positive emotion and well-being. *Journal of Research in Personality, 46*(5), 504–516. https://doi.org/10.1016/j.jrp.2012.05.009
Matilla-Santander, N., Martín-Sánchez, J. C., González-Marrón, A., Cartanyà-Hueso, À., Lidón-Moyano, C., & Martínez-Sánchez, J. M. (2020). Precarious employment, unemployment and their association with health-related outcomes in 35 European countries: a cross-sectional study. *Critical Public Health, 31*(4), 404–415. https://doi.org/10.1080/09581596.2019.1701183

Milanovic, M., Ayukawa, E., Usyatynsky, A., Holshausen, K., & Bowie, C. R. (2018). Self efficacy in depression. *The Journal of Nervous and Mental Disease, 206*(5), 350–355. https://doi.org/10.1097/nmd.0000000000000804

Miner, A. S., Schueller, S. M., Lattie, E. G., & Mohr, D. C. (2015). Creation and validation of the Cognitive and Behavioral Response to Stress Scale in a depression trial. *Psychiatry Research, 230*(3), 819-825. https://doi.org/10.1016/j.psychres.2015.10.033

Moulds, M. L., Yap, C. S. L., Kerr, E., Williams, A. D., & Kandris, E. (2010). Metacognitive beliefs increase vulnerability to rumination. *Applied Cognitive Psychology, 24*(3), 351–364. https://doi.org/10.1002/acp.1681

Muller, L., & Spitz, E. (2004). [Multidimensional assessment of coping: validation of the Brief COPE among French population]. *HAL (Le Centre Pour La Communication Scientifique Directe), 29*(6), 507–518. https://hal.univ-lorraine.fr/hal-02936831

Naragon-Gainey, K., McMahon, T. P., & Chacko, T. P. (2017). The structure of common emotion regulation strategies: A meta-analytic examination. *Psychological Bulletin, 143*(4), 384–427. https://doi.org/10.1037/bul0000093

Nolen-Hoeksema, S., Wisco, B. E., & Lyubomirsky, S. (2008). Rethinking rumination. *Perspectives on Psychological Science, 3*(5), 400–424. https://doi.org/10.1111/j.1745-6924.2008.00088.x

Okoroji, C., Gleibs, I. H., & Jovchelovitch, S. (2020). Elite stigmatization of the unemployed: The association between framing and public attitudes. *British Journal of Psychology, 112*(1), 207–229. https://doi.org/10.1111/bjop.12450

Papageorgiou, C., & Wells, A. (2001). Positive beliefs about depressive rumination: Development and preliminary validation of a self-report scale. *Behavior Therapy, 32*(1), 13–26. https://doi.org/10.1016/s0005-7894(01)80041-1
Park, C., Rosenblat, J. D., Lee, Y., Pan, Z., Cao, B., Iacobucci, M., & McIntyre, R. S. (2019).
The neural systems of emotion regulation and abnormalities in major depressive
disorder. *Behavioural Brain Research*, 367, 181–
188. https://doi.org/10.1016/j.bbr.2019.04.002

Paul, K. I., & Moser, K. (2009). Unemployment impairs mental health: Meta-analyses. *Journal of Vocational Behavior, 74*(3), 264–282. https://doi.org/10.1016/j.jvb.2009.01.001

Pavani, J., & Colombo, D. (2023). Appreciation and rumination, not problem solving and avoidance, mediate the effect of optimism on emotional wellbeing. *Personality and Individual Differences, 205*, 112094. https://doi.org/10.1016/j.paid.2023.112094

Pavani, J., Fort, I., Moncel, C., Ritz, H., & Dauvier, B. (2021). Influence of extraversion and neuroticism on the weekly dynamics of jobseekers’ self-regulation. *Journal of Vocational Behavior, 130*, 103618. https://doi.org/10.1016/j.jvb.2021.103618

Pavani, J. B., Larmanou, M., Holtzer, S., & Colombo, D. (2025). Wanting to be Happy Fosters Happiness by Promoting Savoring: A Daily Diary Study. *Journal of Happiness Studies, 26*(3), 45. https://doi.org/10.1007/s10902-025-00878-4

R Core Team (2024) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing.
https://www.R-project.org/

Rezayat, F., & Dehgannayeri, N. (2013). Relationship between Depression and Self-efficacy in Nursing Students. *Iran Journal of Nursing, 26*(81), 54–63. http://ijn.iums.ac.ir/article-1-1534-en.html

Rosseel, Y. (2012). lavaan: AnRPackage for Structural Equation Modeling. *Journal of Statistical Software, 48*(2). https://doi.org/10.18637/jss.v048.i02

Schoemann, A. M., Boulton, A. J., & Short, S. D. (2017). Determining power and sample size for simple and complex mediation models. *Social Psychological and Personality Science, 8*(4), 379–386. https://doi.org/10.1177/1948550617715068

Sheppes, G., Scheibe, S., Suri, G., Radu, P., Blechert, J., & Gross, J. J. (2012). Emotion regulation choice: A conceptual framework and supporting evidence. *Journal of
*Experimental Psychology General*, 143(1), 163-
181. https://doi.org/10.1037/a0030831

Skärlund, M., Åhs, A., & Westerling, R. (2012). Health-related and social factors predicting non-reemployment amongst newly unemployed. *BMC Public Health*, 12(1). https://doi.org/10.1186/1471-2458-12-893

Sloan, E., Hall, K., Moulding, R., Bryce, S., Mildred, H., & Staiger, P. K. (2017). Emotion regulation as a transdiagnostic treatment construct across anxiety, depression, substance, eating and borderline personality disorders: A systematic review. *Clinical Psychology Review*, 57, 141–163. https://doi.org/10.1016/j.cpr.2017.09.002

Sörman, K., Garke, M. Å., Isacsson, N. H., Jangard, S., Bjureberg, J., Hellner, C., Sinha, R., & Jayaram-Lindström, N. (2021). Measures of emotion regulation: Convergence and psychometric properties of the difficulties in emotion regulation scale and emotion regulation questionnaire. *Journal of Clinical Psychology*, 78(2), 201–217. https://doi.org/10.1002/jclp.23206

Takano, K., Van Grieken, J., & Raes, F. (2019). Difficulty in updating positive beliefs about negative cognition is associated with increased depressed mood. *Journal of Behavior Therapy and Experimental Psychiatry*, 64, 22–30. https://doi.org/10.1016/j.jbtep.2019.02.001

Tamir, M., Bigman, Y. E., Rhodes, E., Salerno, J., & Schreier, J. (2014). An expectancy-value model of emotion regulation: Implications for motivation, emotional experience, and decision making. *Emotion*, 15(1), 90–103. https://doi.org/10.1037/emo0000021

The Organization for Economic Cooperation and Development, O. (2024). *Unemployment rate*. Retrieved February 28, 2025, from https://www.oecd.org/en/data/indicators/unemployment-rate.html?oecdcontrol-4c072e451c-var3=2024

Vancea, M., & Utzet, M. (2016). How unemployment and precarious employment affect the health of young people: A scoping study on social determinants. *Scandinavian Journal of Public Health*, 45(1), 73–84. https://doi.org/10.1177/1403494816679555
Vancouver, J. B. (2000). Self-Regulation in organizational settings. In *Elsevier eBooks* (pp. 303–341). https://doi.org/10.1016/b978-012109890-2/50039-1

Vancouver, J. B., & Purl, J. D. (2016). A computational model of self-efficacy's various effects on performance: Moving the debate forward. *Journal of Applied Psychology, 102*(4), 599–616. https://doi.org/10.1037/apl0000177

Vanderlind, W. M., Millgram, Y., Baskin-Sommers, A. R., Clark, M. S., & Joormann, J. (2020). Understanding positive emotion deficits in depression: From emotion preferences to emotion regulation. *Clinical Psychology Review, 76*, 101826. https://doi.org/10.1016/j.cpr.2020.101826

Vinokur, A. D., & Price, R. H. (2015). Promoting reemployment and mental health among the unemployed. In *Aligning perspectives on health, safety and well-being* (pp. 171–186). https://doi.org/10.1007/978-94-017-9798-6_10

Virgolino, A., Costa, J., Santos, O., Pereira, M. E., Antunes, R., Ambrósio, S., Heitor, M. J., & Carneiro, A. V. (2022). Lost in transition: a systematic review of the association between unemployment and mental health. *Journal of Mental Health, 31*(3), 432–444. https://doi.org/10.1080/09638237.2021.2022615

Visted, E., Vøllestad, J., Nielsen, M. B., & Schanche, E. (2018). Emotion Regulation in Current and Remitted Depression: A Systematic Review and Meta-Analysis. *Frontiers in Psychology, 9*. https://doi.org/10.3389/fpsyg.2018.00756

Wanberg, C. R. (2011). The individual experience of unemployment. *Annual Review of Psychology, 63*(1), 369–396. https://doi.org/10.1146/annurev-psych-120710-100500

Wang, L., & Yan, F. (2018). Emotion regulation strategy mediates the relationship between goal orientation and job search behavior among university seniors. *Journal of Vocational Behavior, 108*, 1–12. https://doi.org/10.1016/j.jvb.2018.05.011

Watkins, E., & Moulds, M. (2005). Distinct modes of ruminative self-focus: Impact of abstract versus concrete rumination on problem solving in depression. *Emotion, 5*(3), 319–328. https://doi.org/10.1037/1528-3542.5.3.319
Webb, T. L., Miles, E., & Sheeran, P. (2012). Dealing with feeling: A meta-analysis of the effectiveness of strategies derived from the process model of emotion regulation. *Psychological Bulletin*, 138(4), 775-808. https://doi.org/10.1037/a0027600

Wells, A. (2010). Metacognitive theory and therapy for worry and generalized anxiety disorder: Review and status. *Journal of Experimental Psychopathology, 1*(1), 133–145. https://doi.org/10.5127/jep.007910

Wege, N., Angerer, P., & Li, J. (2017). Effects of lifetime unemployment experience and job insecurity on Two-Year Risk of Physician-Diagnosed Incident Depression in the German working population. *International Journal of Environmental Research and Public Health*, 14(8), 904. https://doi.org/10.3390/ijerph14080904

Wickham, H. (2016). ggplot2. In *Use R!* https://doi.org/10.1007/978-3-319-24277-4
<caption>Table 1</caption>

Descriptive statistics and intercorrelations between our main variables of interest for Study 1.

<table><thead><tr><th></th><th>M</th><th>SD</th><th>Sk.</th><th>1</th><th>2</th><th>3</th><th>4</th><th>5</th><th>6</th><th>7</th><th>8</th><th>9</th></tr></thead><tbody><tr><td>R-U</td><td>2.21</td><td>0.7</td><td>0.2</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>R-SE</td><td>55.2</td><td>23.6</td><td>-0.17</td><td>0.49</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>R-UV</td><td>46.9</td><td>23.8</td><td>0.1</td><td>0.49</td><td>0.53</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>CR-U</td><td>2.24</td><td>0.8</td><td>0.5</td><td>0.08</td><td>-0.009</td><td>0.13</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>CR-SE</td><td>55.8</td><td>26.5</td><td>-0.14</td><td>-0.08</td><td>-0.06</td><td>0.04</td><td>0.62</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>CR-UV</td><td>63.1</td><td>25.6</td><td>-0.54</td><td>0.02</td><td>0.06</td><td>-0.03</td><td>0.55</td><td>0.67</td><td></td><td></td><td></td><td></td></tr><tr><td>PS-E</td><td>2.45</td><td>0.89</td><td>0.07</td><td>0.11</td><td>0.02</td><td>0.09</td><td>0.21</td><td>0.1</td><td>0.06</td><td></td><td></td><td></td></tr><tr><td>PS-SE</td><td>61.3</td><td>27.5</td><td>-0.37</td><td>-0.09</td><td>-0.02</td><td>0.08</td><td>0.09</td><td>0.28</td><td>0.11</td><td>0.64</td><td></td><td></td></tr><tr><td>PS-UV</td><td>65</td><td>25.3</td><td>-0.49</td><td>-0.03</td><td>0.03</td><td>0.01</td><td>0.05</td><td>0.14</td><td>0.2</td><td>0.52</td><td>0.67</td><td></td></tr><tr><td>DL</td><td>0.9</td><td>0.13</td><td>0.35</td><td>0.51</td><td>0.33</td><td>0.27</td><td>-0.27</td><td>-0.34</td><td>-0.25</td><td>-0.1</td><td>-0.17</td><td>-0.1</td></tr></tbody></table>

Note. M.: mean; SD.: standard-deviation; Sk.: skewness; R-U: Rumination use; RSE: Rumination self-efficacy; R-UV: Rumination utility value; CR-U: Cognitive Reappraisal use; CR-SE: Cognitive Reappraisal self-efficacy; CR-U: Cognitive Reappraisal utility value. PS-U: Problem Solving use; PSSE: Problem Solving self-efficacy; PS-UV: Problem Solving utility value; DL: Depression Level. With a threshold set at *p* = 0.05, all correlations located below the diagonal exceeding the absolute value of *r* = 0.10 were statistically significant, while all correlations above the diagonal were statistically significant.
Table S1

Descriptive statistics and intercorrelations between depression and the variables related to rumination for Study 1.

<table><thead><tr><th></th><th>M</th><th>SD</th><th>Sk</th><th>1</th><th>2</th><th>3</th></tr></thead><tbody><tr><td>1. R-U</td><td>2.21</td><td>0.7</td><td>0.2</td><td></td><td></td><td></td></tr><tr><td>2. R-SE</td><td>55.2</td><td>23.6</td><td>-0.17</td><td>0.49</td><td></td><td></td></tr><tr><td>3. R-UV</td><td>46.9</td><td>23.8</td><td>0.1</td><td>0.49</td><td>0.53</td><td></td></tr><tr><td>4. DL</td><td>0.9</td><td>0.13</td><td>0.35</td><td>0.51</td><td>0.33</td><td>0.27</td></tr></tbody></table>

Table S2

Descriptive statistics and intercorrelations between depression and the variables related to cognitive reappraisal for Study 1.

<table><thead><tr><th></th><th>M</th><th>SD</th><th>Sk</th><th>1</th><th>2</th><th>3</th></tr></thead><tbody><tr><td>1. CR-U</td><td>2.24</td><td>0.8</td><td>0.5</td><td></td><td></td><td></td></tr><tr><td>2. CR-SE</td><td>55.2</td><td>26.5</td><td>-0.14</td><td>0.62</td><td></td><td></td></tr><tr><td>3. CR-UV</td><td>63.1</td><td>25.6</td><td>-0.54</td><td>0.55</td><td>0.67</td><td></td></tr><tr><td>4. DL</td><td>0.9</td><td>0.13</td><td>0.35</td><td>-0.27</td><td>-0.34</td><td>-0.25</td></tr></tbody></table>

Table S3

Descriptive statistics and intercorrelations between depression and the variables related to problem solving for Study 1.

<table><thead><tr><th></th><th>M</th><th>SD</th><th>Sk</th><th>1</th><th>2</th><th>3</th></tr></thead><tbody><tr><td>1. PS-U</td><td>2.85</td><td>0.89</td><td>0.07</td><td></td><td></td><td></td></tr><tr><td>2. PS-SE</td><td>61.3</td><td>27.5</td><td>-0.37</td><td>0.64</td><td></td><td></td></tr><tr><td>3. PS-UV</td><td>65</td><td>25.3</td><td>-0.49</td><td>0.52</td><td>0.67</td><td></td></tr><tr><td>4. DL</td><td>0.9</td><td>0.13</td><td>0.35</td><td>-0.10</td><td>-0.17</td><td>-0.1</td></tr></tbody></table>
<caption>Table 2.</caption>

Mediation analyses of the effect of the relationship between depression level and strategy use via self-efficacy in using the strategy and utility value of using it for Study 1.

<table><thead><tr><th>Effect</th><th>β</th><th>SE</th><th>p</th></tr></thead><tbody><tr><td>Relationship R&D - Total effect</td><td><strong>0.59</strong></td><td>0.04</td><td><strong>&lt;0.001***</strong></td></tr><tr><td>Relationship R&D - Direct effect</td><td>0.43</td><td>0.05</td><td><strong>&lt;0.001***</strong></td></tr><tr><td>Relationship R&D - Indirect effect via UV</td><td><strong>0.07</strong></td><td>0.02</td><td><strong>&lt;0.001***</strong></td></tr><tr><td>Relationship R&D - Indirect effect via SE</td><td>0.08</td><td>0.02</td><td><strong>&lt;0.001***</strong></td></tr><tr><td>Relationship CR&D - Total effect</td><td>-0.40</td><td>0.06</td><td><strong>&lt;0.001***</strong></td></tr><tr><td>Relationship CR&D - Direct effect</td><td>-0.14</td><td>0.05</td><td><strong>&lt;0.001***</strong></td></tr><tr><td>Relationship CR&D - Indirect effect via UV</td><td><strong>-0.07</strong></td><td>0.02</td><td><strong>&lt;0.001***</strong></td></tr><tr><td>Relationship CR&D - Indirect effect via SE</td><td>-0.17</td><td>0.03</td><td><strong>&lt;0.001***</strong></td></tr><tr><td>Relationship PS&D - Total effect</td><td>-0.16</td><td>0.06</td><td><strong>&lt;0.01**</strong></td></tr><tr><td>Relationship PS&D - Direct effect</td><td>-0.072</td><td>0.05</td><td>0.14</td></tr><tr><td>Relationship PS&D - Indirect effect via UV</td><td>-0.017</td><td>0.01</td><td>0.18</td></tr><tr><td>Relationship PS&D - Indirect effect via SE</td><td>-0.07</td><td>0.03</td><td><strong>0.04*</strong></td></tr></tbody></table>

Note. SE: standard error; R: Rumination; CR: Cognitive reappraisal; PS: Problem solving; D: Depression; UV: Utility Value; SE: Self-Efficacy. Statistically significant effects are set in bold.
<caption>Table 3</caption>

Descriptive statistics and intercorrelations between our main variables of interest for Study 2.

<table><thead><tr><th></th><th>M</th><th>SD</th><th>Sk.</th><th>1</th><th>2</th><th>3</th><th>4</th><th>5</th><th>6</th><th>7</th><th>8</th><th>9</th></tr></thead><tbody><tr><td>R-U</td><td>2.33</td><td>0.72</td><td>0.06</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>R-SE</td><td>57.9</td><td>25.3</td><td>-0.24</td><td>0.42</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>R-UV</td><td>52.1</td><td>23.8</td><td>-0.001</td><td>0.37</td><td>0.49</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>CR-U</td><td>2.25</td><td>0.83</td><td>0.41</td><td>0.07</td><td>0.03</td><td>0.06</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>CR-SE</td><td>57.8</td><td>27.9</td><td>-0.16</td><td>0.01</td><td>-0.02</td><td>0.02</td><td>0.64</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>CR-UV</td><td>64.2</td><td>25.3</td><td>-0.48</td><td>0.01</td><td>0.003</td><td>-0.06</td><td>0.54</td><td>0.67</td><td></td><td></td><td></td><td></td></tr><tr><td>PS-E</td><td>2.5</td><td>0.89</td><td>0.09</td><td>0.09</td><td>0.14</td><td>0.11</td><td>0.28</td><td>0.09</td><td>0.06</td><td></td><td></td><td></td></tr><tr><td>PS-SE</td><td>62.4</td><td>26.6</td><td>-0.38</td><td>-0.005</td><td>0.09</td><td>0.11</td><td>0.14</td><td>0.25</td><td>0.11</td><td>0.66</td><td></td><td></td></tr><tr><td>PS-UV</td><td>64.2</td><td>25.6</td><td>-0.63</td><td>0.006</td><td>0.08</td><td>0.1</td><td>0.06</td><td>0.09</td><td>0.21</td><td>0.6</td><td>0.73</td><td></td></tr><tr><td>DL</td><td>0.98</td><td>0.14</td><td>0.41</td><td>0.42</td><td>0.27</td><td>0.25</td><td>-0.38</td><td>-0.36</td><td>-0.32</td><td>-0.12</td><td>-0.07</td><td>-0.1</td></tr></tbody></table>

*Note.* M.: mean; SD.: standard-deviation; Sk.: skewness; R-U: Rumination use; RSE: Rumination self-efficacy; R-UV: Rumination utility value; CR-U: Cognitive Reappraisal use; CR-SE: Cognitive Reappraisal self-efficacy; CR-U: Cognitive Reappraisal utility value. PS-U: Problem Solving use; PSSE: Problem Solving self-efficacy; PS-UV: Problem Solving utility value; DL: Depression Level. With a threshold set at *p* = 0.05, all correlations located below the diagonal exceeding the absolute value of *r* = 0.10 were statistically significant, while all correlations above the diagonal were statistically significant.
Table S5

Descriptive statistics and intercorrelations between depression and the variables related to rumination for Study 2.

<table><thead><tr><th></th><th>M</th><th>SD</th><th>Sk</th><th>1</th><th>2</th><th>3</th></tr></thead><tbody><tr><td>1. R-U</td><td>2.33</td><td>0.72</td><td>0.06</td><td></td><td></td><td></td></tr><tr><td>2. R-SE</td><td>57.9</td><td>25.3</td><td>-0.24</td><td>0.42</td><td></td><td></td></tr><tr><td>3. R-UV</td><td>52.1</td><td>23.8</td><td>-0.001</td><td>0.37</td><td>0.49</td><td></td></tr><tr><td>4. DL</td><td>0.98</td><td>0.14</td><td>0.41</td><td>0.42</td><td>0.27</td><td>0.25</td></tr></tbody></table>

Table S6

Descriptive statistics and intercorrelations between depression and the variables related to cognitive reappraisal for Study 2.

<table><thead><tr><th></th><th>M</th><th>SD</th><th>Sk</th><th>1</th><th>2</th><th>3</th></tr></thead><tbody><tr><td>1. CR-U</td><td>2.24</td><td>0.8</td><td>0.5</td><td></td><td></td><td></td></tr><tr><td>2. CR-SE</td><td>55.2</td><td>26.5</td><td>-0.14</td><td>0.64</td><td></td><td></td></tr><tr><td>3. CR-UV</td><td>63.1</td><td>25.6</td><td>-0.54</td><td>0.54</td><td>0.57</td><td></td></tr><tr><td>4. DL</td><td>0.9</td><td>0.13</td><td>0.35</td><td>-0.38</td><td>-0.36</td><td>-0.32</td></tr></tbody></table>

Table S7

Descriptive statistics and intercorrelations between depression and the variables related to problem solving for Study 2.

<table><thead><tr><th></th><th>M</th><th>SD</th><th>Sk</th><th>1</th><th>2</th><th>3</th></tr></thead><tbody><tr><td>1. PS-U</td><td>2.85</td><td>0.89</td><td>0.07</td><td></td><td></td><td></td></tr><tr><td>2. PS-SE</td><td>61.3</td><td>27.5</td><td>-0.37</td><td>0.66</td><td></td><td></td></tr><tr><td>3. PS-UV</td><td>65</td><td>25.3</td><td>-0.49</td><td>0.6</td><td>0.73</td><td></td></tr><tr><td>4. DL</td><td>0.9</td><td>0.13</td><td>0.35</td><td>-0.12</td><td>-0.07</td><td>-0.1</td></tr></tbody></table>

Table 4.
Mediation analyses of the effect of the relationship between depression level and strategy use via self-efficacy in using the strategy and utility value of using it for Study 2.

<table><thead><tr><th>Effect</th><th>β</th><th>SE</th><th>p</th></tr></thead><tbody><tr><td>Relationship R&D - Total effect</td><td><strong>0.53</strong></td><td>0.05</td><td><strong>&lt;0.001***</strong></td></tr><tr><td>Relationship R&D - Direct effect</td><td>0.40</td><td>0.05</td><td><strong>&lt;0.001***</strong></td></tr><tr><td>Relationship R&D - Indirect effect via UV</td><td><strong>0.04</strong></td><td>0.02</td><td><strong>&lt;0.05*</strong></td></tr><tr><td>Relationship R&D - Indirect effect via SE</td><td>0.08</td><td>0.02</td><td><strong>&lt;0.001***</strong></td></tr><tr><td>Relationship CR&D - Total effect</td><td>-0.46</td><td>0.05</td><td><strong>&lt;0.001***</strong></td></tr><tr><td>Relationship CR&D - Direct effect</td><td>-0.20</td><td>0.05</td><td><strong>&lt;0.001***</strong></td></tr><tr><td>Relationship CR&D - Indirect effect via UV</td><td>-0.07</td><td>0.02</td><td><strong>&lt;0.05*</strong></td></tr><tr><td>Relationship CR&D - Indirect effect via SE</td><td>-0.19</td><td>0.03</td><td><strong>&lt;0.001***</strong></td></tr><tr><td>Relationship PS&D - Total effect</td><td>-0.07</td><td>0.07</td><td>0.33</td></tr><tr><td>Relationship PS&D - Direct effect</td><td>0.002</td><td>0.05</td><td>0.08</td></tr><tr><td>Relationship PS&D - Indirect effect via UV</td><td>-0.05</td><td>0.02</td><td>0.97</td></tr><tr><td>Relationship PS&D - Indirect effect via SE</td><td>-0.025</td><td>0.03</td><td>0.3</td></tr></tbody></table>

Note. SE: standard error; R: Rumination; CR: Cognitive reappraisal; PS: Problem solving; D: Depression; UV: Utility Value; SE: Self-Efficacy. Statistically significant effects are set in bold.