dataesr/scientific-paragraphs-categorization huggingface dataset:
5, and Infit values between 0. 7-1. 3 indicate sufficient item fit  [59] [60] [61]. Differential item functioning (DIF) refers to differences in item performance between the respondent groups we match with respect to the construct we measure. We matched groups on gender, age, education, status of employment, financial deprivation, social level, and/or general health status. We refer to uniform DIF when items have different relative difficulty and non-uniform DIF when items discriminate differently for different groups of people. An overview of person factor categories is available in Table  S1  of the Supplementary file. DIF was evaluated by using two-way analysis of variance  [62]  and inspecting graphical displays. We used a Bonferroni-adjusted significant probability value of <5%. Since models tested on large data sets run the risk of being rejected due to the chisquared statistic  [63] , analyses on data-model fit, item fit, and DIF were based on reduced sample sizes.
For (b), linear regression analyses were performed including the NAV-HL score and the mentioned social variables as predictor variables of general health status. In this regard, the aim was not to examine an optimal model that best explains the dependent variable, but to examine whether NAV-HL is related to the considered factors as expected. As sample sizes in CH (CATI: n = 192) and CZ (CATI: n = 532) were considerably smaller compared to other countries, in this case, data from the total country samples were used.
Results The sample characteristics are presented in Table  3. In total, valid data on NAV-HL were collected for an overall of 15,685 respondents across the eight countries. In most countries, the number of missing values (cases not used to calculate the score because less than 80% of HLS 19 NAV items were answered) was small, varying between 0% and 2%. In AT (5%), CH (10% for CATI), CZ (10% for CATI), and PT (14%), missing values were higher.
Confirmatory Factor Analysis Fitting the single-factor CFA (Table  4 ) to dichotomous data, most goodness-of-fit indices indicated good to sufficient fit for most countries. SRMR varied between the acceptable values 0. 03 (CZ, CAWI) and 0. 07 (CH, CAWI, and DE), while the RMSEA of 0. 07 observed for BE, CH (CAWI), and PT was slightly above the strict target value ≤0. 06  [67]. With a minimum of 0. 97, the observed values for the CFI, TLI, GFI, and AGFI are sufficient. The CFA analysis based on polytomous data points to similar results. The fit indices for the model remained stable except for the RMSEA, where values were considerably higher for polytomous data. For the dichotomous data, the standardized parameter estimates are above 0. 7 for all items except for the German data and for item HLS 19 -NAV9 (to understand how to get an appointment with a particular health service) in AT, BE, CH, and FR.
The CFA for the polytomous data shows comparable results with only minor differences in the range of (-0. 05 to 0. 06). Except for the German data, the R 2 value is above 0. 5 for all items but HLS 19 -NAV9, with the R 2 values being minimally higher on average (by 0. 02) for the dichotomous data. For all country-specific samples, there were entries >0. 10 [68]  in the residual correlation matrix. Of the 66 possible residual correlation coefficients (cf. Tables  S2  and S3 ), on average, 6 values are above 0. 1. For the dichotomous data, the number of residual correlation coefficients above 0. 1 is the highest for CH (CAWI: 12 times), DE (11 times), BE (8 times), CH (CATI: 7 times), and PT (7 times). The residual correlation coefficients are above 0. 1 for the majority of data sets for HLS  19  -NAV1 with HLS 19 -NAV2.
The residual correlation coefficients of HLS 19 -NAV1 or HLS  19  -NAV2 with HLS 19 -NAV7 or HLS 19 -NAV8 could also require further inspection. For the polytomous data, the data sets of BE, CH (CAWI, CATI), and DE show 10 or more reidual correlation coefficients that are higher than 0. 1. The variables concerned are the same as for the dichotomous data. All residual correlation coefficients are below 0. 2 with the exception of the residual correlation of HLS 19 -NAV11 with HLS 19 -NAV10 for the Swiss (CATI) polytomous data (r res = 0. 2). Fitting the two-factor CFA model (Table  5 ) to dichotomous data, fit indices improved compared to the single-factor CFA, with lower SRMR values and/or RMSEA values in AT (SRMR = 0. 04; RMSEA = 0. 04), BE (SRMR = 0. 05; RMSEA = 0. 05), CH (SRMR = 0. 06; RMSEA = 0.
Rasch Analyses at the Overall Level When performing Rasch modelling based on dichotomous as opposed to polytomous data, it became evident, as expected, that the power of analyses of fit and reliability indices decreased and that the proportion of respondents with extreme scores increased (Table  S4 ). Thus, it was decided to report results from Rasch modelling (with exception to PSI) based on polytomous HLS 19 -NAV data (taken from Guttersrud et al. [47] ). With a reduced sample size (n = 720: 20 persons per threshold), good overall datamodel fit for the HLS 19 -NAV was observed in AT (χ 2 : p > 0. 05), and sufficient overall data-model fit (χ 2 : p > 0. 01) was observed in CH (CAWI, CATI), CZ (CAWI, CATI), and DE (Table  6 ). When sample size was further reduced (n = 360: 10 persons per threshold), data from BE, PT, and SI (CAWI, CAPI) also displayed sufficient to good data-model fit.
The data collected in FR did not fit well to the PCM. Within countries, the distribution of HLS 19 -NAV item threshold locations was welltargeted at the distribution of person locations, with mean person location varying between -0. 31 (DE) and 0. 96 (SI, CAWI). Testing for dimensionality revealed that the HLS 19 -NAV scale is not strictly unidimensional: Using dependent t-tests, between 4. 2% (CZ, CATI) and 12. 2% (DE) of respondents obtained significantly different scores or proficiency estimates on the organizational level and system level subscales. Thus, too many respondents obtained too different subscales scores to claim that the two subscales measure the same trait.
AT: The Austrian Health Literacy Survey was commissioned and financed by the Austrian Federal Health Agency and the Federation of Austrian Social Insurance Institutions. BE: The data collection for Belgium (NL and FR) was funded by the Union Nationale des Mutualités Libres (MLOZ). CH: The national HL survey was funded by the Swiss Federal Office of Public Health (FOPH). CZ: Data collection was jointly funded by (all seven) Czech health insurance funds. DE: The German study was funded by the German Federal Ministry of Health, grant number: Kapitel 1504 Titel 54401, ZMV I 1-2518 004 (HLS-GER 2). FR: The research was supported by the National Public Health agency (Santé Publique France, 21DPPA040-0) and by Ligue contre le cancer (LIGUE2019). NO: The Norwegian HLS 19 was commissioned and financed by the Norwegian Ministry of Health and Care Services.
Acknowledgments: The authors would like to acknowledge  The International Coordination Centre (ICC)  of the  HLS 19 Project  for project coordination. We also want to thank all national HLS 19 team members who contributed to the HLS 19 Project by contributing to the chapter on NAV-HL in the International Report or/and providing country data for analyses. We especially thank  Stephan Van den Broucke  and  Zdenek Kucera as PIs of BE  and CZ for using national data on NAV-HL. We also acknowledge  Kjell Sverre Pettersen  (Norwegian HLS 19 study team), who contributed by performing Rasch modelling of data used for this article [47] and  Alexander Haarmann  for proofreading the manuscript.
There is a large amount of variability in performance in masked-speech reception tasks, as well as in psychophysical auditory temporal processing tasks, between listeners with normal or relatively normal low-frequency hearing. In this study we used a cross-sectional dataset collected on 102 listeners (34 young, 34 middle-aged, 34 older) to assess whether variance in these tasks could be explained by variance in subcortical electrophysiological measures of auditory function (auditory brainstem responses and frequency following responses), and whether variance in speechreception performance could be explained by variance in auditory temporal processing tasks. The potential confounding effect of high-frequency sensitivity was strictly controlled for by using highpass masking noise. Because each high-level construct (masked-speech reception, auditory temporal processing, and subcortical electrophysiological function) was indexed by several variables, we used principal component analyses to reduce the dimensionality of the dataset.
, 2016; Valderrama et al. , 2018; Mepani et al. , 2020) , while other studies have failed to find evidence for such associations  (Bramhall et al. , 2018; Guest et al. , 2018; Schoof and Rosen, 2016; Johannesen et al. , 2019; Prendergast et al. , 2017 Prendergast et al. , , 2019;; Smith et al. , 2019). Similarly, associations between psychophysical measures of auditory temporal processing and electrophysiological measures of subcortical auditory function have been found by some studies  (Bharadwaj et al. , 2015; Verhulst et al. , 2018) , but not others  (Prendergast et al. , 2017; Paul et al. , 2017). In order to elucidate the relations between these measures, in the current study we exploited a rich dataset that we previously analyzed with the aim of finding signs of age-related cochlear synaptopathy (CS), or more generally, of age-related declines that could not be accounted for by loss of hearing sensitivity  (Carcagno and Plack, 2020, 2021).
This dataset includes tests of abilities close to real-world hearing abilities (speech reception, and assessment of preference for musical consonance, which for brevity will be referred to as tests of real-world hearing abilities), psychophysical tests of auditory temporal processing, and electrophysiological tests of subcortical auditory processing collected on a cross-sectional sample of 102 participants ranging in age from 19 to 74 years. In the current study, we used this dataset to answer the following questions: i) to what extent can measures of real-world hearing abilities be predicted by electrophysiological measures of subcortical auditory function? ii) to what extent can measures of real-world hearing abilities be predicted by psychophysical measures of auditory temporal processing? and, iii) to what extent can psychophysical measures of auditory temporal processing be predicted by electrophysiological measures of subcortical auditory function? These questions are important because associations between the three constructs mentioned above can provide insights into the mechanisms underlying individual differences in auditory abilities.
The dataset we used in the current study is ideally suited to address the above questions not only because it contains measurements for each of the three constructs (real-world hearing abilities, psychophysical temporal processing, and subcortical auditory function) collected from a large sample of participants, but also because each construct was assessed using several different tests, at different stimulus levels and in some cases different stimulus frequencies, thus providing a comprehensive assessment of each construct. Given the large number of different measures used to assess each construct, in order to improve the interpretability of the results we used principal component analysis (PCA;  Sharma, 1996; Jolliffe, 2002)  to reduce the number of variables while preserving the largest sources of variance in the measures.
The dataset also contains measurements of audiometric thresholds, cognitive abilities, and noise exposure, thus allowing us to control statistically for the potential confounding effects that these variables may have on the associations between the different constructs. Moreover, we took two additional measures in the collection of the dataset to minimize the potentially confounding effect of high-frequency hearing sensitivity on behavioral and electrophysiological measures. These measures included selecting listeners with relatively preserved hearing sensitivity within a low-frequency region below ∼ 4 kHz, and presenting the stimuli within this low-frequency region in the presence of high-pass masking noise to eliminate the contribution of higher cochlear frequency regions to the electrophysiological or behavioral responses.
Participants A total of 170 participants (129 females) from three age groups (young: 18-39, middle-aged: 40-59, older: >60 years old) were enrolled in the study. Sixty-eight participants either failed to meet the selection criteria outlined below, or withdrew from the study. Only the data of the 102 participants who completed the study will be presented. Selection criteria included audiometric thresholds for both ears below 20 dB HL at octave frequencies from 0. 125 to 2 kHz, and below 40 dB HL at 4 kHz. No selection criteria were imposed for frequencies above 4 kHz. Due to the use of an incorrect calibration table for the headphones used in the audiometric tests the actual cutoff thresholds differed by a few dBs with respect to the nominal cutoff thresholds listed above. Using the correct calibration table five older, two middle-aged, and two young participants would not have passed the selection.
However, these listeners had thresholds below 30. 5 dB HL for audiometric frequencies up to 2 kHz, and below 37 dB HL at 4 kHz. Given that their thresholds were only slightly above the cutoff criteria, and given that audiometric thresholds were used as continuous covariates, the data of these listeners were included in the analyses. Participants with audiometric threshold asymmetries between the left and right ear larger than 20 dB at any frequency from 0. 125 to 4 kHz were excluded from the study. An otoscopic examination was performed prior to the beginning of the tests, and participants with earwax occlusions were excluded from the study. Participants were required to be native British English speakers. Recruitment continued until 34 participants from each age group had completed the study.
Speech reception measures Speech reception was assessed with the coordinate response measure (CRM;  Bolia et al. , 2000) , and the digit triplets test (DTT;  Smits et al. , 2004). For the CRM, thresholds were measured both with speech maskers colocated (CNT) with the target speech (that was always presented at a 0°azimuth), or offset (OFF) by a ±65°azimuth. Spatialization was achieved by convolving the sentences with the head-related impulse responses of subject #3 from the CIPIC database  (Algazi et al. , 2002). The DTT test was run with a noise lowpass filtered at 3 kHz. The noise was intended to be a speech-shaped noise, but due to a bug in the software used to generate it, it had a slightly different spectral shape (see  Carcagno and Plack, 2021).
PCA requires either data without missing values or the imputation of missing values. Both ABR amplitudes and latencies had some missing values due to peaks or troughs buried within the noise floor. For wave amplitudes the cause of missing values could be reasonably imputed to a very low underlying wave amplitude, and missing amplitude values were imputed as the lowest recorded amplitude value in the dataset (0. 38 nV), which is practically close to zero. For wave latencies the reason for missing data was the same (low wave amplitude value), but an estimate of the underlying latency value cannot be inferred from this information. Improper imputation or an analysis limited to the cases with complete data could lead to biased results  (Gelman and Hill, 2007). For this reason, the ABR wave latencies were not included in the current analyses.
Principal component analyses PCAs were computed in R (R Core Team, 2021) with the FactoMineR package  (Lê et al. , 2008). Variables were standardized before being entered into the PCAs. To address the main research questions presented in the Introduction four PCAs were run: i) one on the six variables indexing speech reception; ii) one on the two variables indexing consonance preference; iii) one on the 18 variables indexing psychophysical auditory temporal processing; iv) one on the 28 variables indexing the electrophysiological measures (ABR and FFR amplitudes). The number of PCs to retain was determined via parallel analysis perfomed with the psych R package  (Revelle, 2019). Parallel analysis compares the eigenvalues of the PCA performed on a given dataset with those obtained from PCAs performed on random uncorrelated datasets of the same dimension and with the same sample size.
Discussion In this study we assessed the relations between a large set of behavioral and electrophysiological measures that were collected for an investigation of age-related hearing declines on 102 listeners across the age range  (Carcagno and Plack, 2020, 2021). In order to deal with the issue of the large number of variables assessed, some of which measured the same constructs, we used PCA to reduce the dimensionality of the dataset while preserving the largest sources of variance within the different domains to which the variables belonged (speech reception, consonance preference, psychophysical temporal processing, and electrophysiological subcortical function measures). We found that increases (reflecting worse thresholds) in the first PC of the psychophysical temporal processing measures were credibly associated with increases (reflecting worse thresholds) of the first speech-reception PC independently of age, low-frequency audiometric thresholds, lifetime noise exposure, cognitive abilities, and musical experience.
Before discussing these results, it is important to point out that the reason why few sizeable associations were found in the current study is not linked to the choices of using PCA to compress the dimensionality of the dataset and of selecting only a restricted number of components, as suggested by parallel analysis. Further exploratory analyses (not reported in the paper for brevity) in which all PCs explaining at least 10% of the variance were included did not reveal any new associations. Also, inspection of the correlation matrix between the different measures, shown in Table  S16 , does not suggest that testing each individual variable instead of compressing the dataset using PCA would have led to discovering other sizeable associations. The finding of a relation between speech-reception and psychophysical temporal processing Schoof and Rosen (2014) found that frequency modulation (FM) detection thresholds, which reflect TFS processing, significantly predicted speech-reception thresholds for IEEE sentences  (Rothauser et al.
Acknowledgments This research was supported by a grant ( BB/M007243/1 ) from the  Biotechnology and Biological Sciences Research Council, UK. Author CP was supported by the  NIHR Manchester Biomedical Research Centre. The electrophysiological data used in this paper are available from https://osf. io/s3bd9/. The behavioral data used in this paper are available from https://osf. io/b69ds/.
Impact of COVID-19 on Distance Learning Practices in Selected Countries of the Mediterranean Region In the survey completed by 12 participating VEEs belonging to 7 countries of the Mediterranean region (Bosnia and Herzegovina, Italy, Jordan, France, Tunisia, Morocco, Turkey) conducted at the beginning and during COVID-19, data were collected regarding DL methods during three consequent periods: • before COVID-19 closure of the universities; • during the closure of universities; and • after autumn of 2020 (partial opening). The survey revealed the following findings (Table  1 ): • Protection of staff, students, and clients from the epidemic were the priority of university management. • DL resources existed and were partially applied in most of the VEEs before the pandemic. • A systematic approach was not in place and a variety of IT platforms were used.
Study design: Pregnant women aged 15-49 years with a conception date 1/1/2018 to 31/12/2018 were included in this population-based cross-sectional study, using routine healthcare datasets from primary care: Clinical Practice Research Datalink (CPRD, United Kingdom, n = 37,641) and Secure Anonymized Information Linkage databank (SAIL, Wales, n = 27,782), and secondary care: Scottish Morbidity Records with linked community prescribing data (SMR, Tayside and Fife, n = 6099). Pre-existing multimorbidity preconception was defined from 79 long-term health conditions prioritised through a workshop with patient representatives and clinicians.
Results: The prevalence of multimorbidity was 44. 2% (95% CI 43. 7-44. 7%), 46. 2% (45. 6-46. 8%) and 19. 8% (18. 8-20. 8%) in CPRD, SAIL and SMR respectively. When limited to health conditions that were active in the year before pregnancy, the prevalence of multimorbidity was still high (24. 2% [23. 8-24. 6%], 23. 5% [23. 0-24. 0%] and 17. 0% [16. 0 to 17. 9%] in the respective datasets). Mental health conditions were highly prevalent and involved 70% of multimorbidity CPRD: multimorbidity with ≥one mental health condition/s 31. 3% [30. 8-31. 8%]).
Despite this, there is a dearth of basic information on the prevalence and types of pre-existing health conditions affecting pregnant women. Better understanding of the epidemiology of multimorbidity in pregnant women could help policy makers and health care providers plan services to prevent women from developing multimorbidity, for early detection and optimal management of health conditions prior to conception, and tailor maternity services to pregnant women with multimorbidity. In the UK, most people are registered with a general practitioner (GP), the gatekeepers to primary care and specialist referrals. In secondary care, health care utilization administrative data are recorded for reimbursement. Thus, both provide good data sources for multimorbidity and pregnancy research. This study aims to describe the epidemiology of preexisting multimorbidity in pregnant women.
Primary care CPRD GOLD contains anonymized, longitudinal medical records for over 19 million patients in the UK (England, Northern Ireland, Scotland and Wales) from over 940 participating general practices; it currently covers 4% of UK GP practices and is widely acknowledged to be representative of the UK population  [6]. It includes data on demographics, diagnoses and prescriptions  [6]. Linkage to area based deprivation index was available for patients in England. Within CPRD GOLD, the CPRD Pregnancy Register is an algorithm that takes information from maternity, antenatal and delivery health records to detect pregnancy episodes and their outcomes  [5]. The SAIL databank is a whole population level database in Wales. It is a repository of anonymized health and socio-economic administrative data and provides linkage at an individual level  [7].
Secondary care and community prescriptions SMR data was available from two Scottish regional health boards: National Health Service (NHS) Tayside and NHS Fife  [8]. A dataset was created linking the Scottish Maternity Records (SMR02) to data from Hospital Admissions (SMR01), Mental Health Inpatients (SMR04), Accident and Emergency, and the Demography and Death registry. This covered diagnoses and demographic data for all inpatient stays and day cases for residents in the two regions. The dataset was also linked to the Prescribing Information System for data on all medications dispensed in the community. Pregnancies were detected from maternity records or pregnancy-related hospital admissions.