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Record W4377023362 · doi:10.3389/fpsyt.2023.1112184

Longitudinal assessment and determinants of short-term and longer-term psychological distress in a sample of healthcare workers during the COVID-19 pandemic in Quebec, Canada

2023· article· en· W4377023362 on OpenAlexaffabout
Filippo Rapisarda, Nicolas Bergeron, Marie‐Michèle Dufour, Stéphane Guay, Steve Geoffrion

Bibliographic record

VenueFrontiers in Psychiatry · 2023
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsUniversité de MontréalCentre Hospitalier de l’Université de MontréalInstitut Universitaire en Santé Mentale de Québec
Fundersnot available
KeywordsDistressMental healthMedicineDescriptive statisticsAnxietyLongitudinal studyDepression (economics)Multinomial logistic regressionPandemicClinical psychologyPsychologyPsychiatryDemographyCoronavirus disease 2019 (COVID-19)DiseaseStatistics

Abstract

fetched live from OpenAlex

Introduction: Previous research has demonstrated the negative impact of the COVID-19 pandemic emergency on the wellbeing of healthcare workers. However, few research contributions reported a longitudinal evaluation of psychological distress and examined determinants of its duration and course over time. The present study aims to explore the impact of the pandemic emergency on HCWs mental health by adopting a longitudinal design and assessing mental health as combination of overlapping clinical symptoms (post-traumatic stress disorder, depression and anxiety). Methods: Data were collected weekly through a mobile application during and after the first wave of COVID-19 in the province of Quebec, Canada, in 2020. Analysis was conducted on a final sample of 382 participants. Participants were grouped into "resilient" (RES) if they did not manifest clinical-level psychological distress during monitoring, "short-term distress" (STD) if distress exceeded the clinical threshold for 1-3 weeks, and longer-term distress (LTD) if it occurred for four or more weeks, even if not consecutively. Descriptive statistics for all variables were computed for each subgroup (RES, STD and LTD), and pairwise comparisons between each group for every descriptive variable were made using chi square statistics for categorical variables and t-test for continuous variables. Predictors of distress groups (STD and LTD vs RES) were assessed running multinomial hierarchical logistic regression models. Results: In our sample, almost two third (59.4%) HCWs did not manifest moderate or severe distress during the monitoring time. Short-term distress, mostly post-traumatic symptoms that lasted for less than 4 weeks, were the most common distress response, affecting almost one third of participants. Longer psychological distress occurred only in a smaller percentage (12.6%) of cases, as a combination of severe posttraumatic, depressive and anxiety symptoms. Perceived occupational stress was the most significant risk factor; moreover individual, peritraumatic work and family risk and protective factors, were likely to significantly affect the stress response. Discussion: Results tend to provide a more complex and resiliency-oriented representation of psychological distress compared to previous cross-sectional studies, but are in line with stress response studies. Findings allow us to better describe the profiles of distress response in STD and LTD groups. Participants that manifest short term distress experience acute stress reaction in which the interplay between personal, family and professional life events is associated with the stress response. Conversely, longer term distress response in HCWs presents a more complex mental health condition with an higher level of impairment and support needs compared to participants with short-term distress.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.095

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.057
GPT teacher head0.415
Teacher spread0.359 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations10
Published2023
Admission routes2
Has abstractyes

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