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Record W4399077200 · doi:10.1080/19338244.2024.2350956

Availability, use, and impact of workplace mental health supports during the COVID-19 pandemic in a Canadian cohort of healthcare workers

2024· article· en· W4399077200 on OpenAlexafffundabout
Shannon M. Ruzycki, Anil Adisesh, Igor Burstyn, Quentin Durand‐Moreau, France Labrèche, Tanis Zadunayski, Nicola Cherry

Bibliographic record

VenueArchives of Environmental & Occupational Health · 2024
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsInstitut de recherche Robert-Sauvé en santé et en sécurité du travailUniversity of TorontoUniversity of AlbertaUniversity of Calgary
FundersCanadian Institutes of Health ResearchCollege of Physicians and Surgeons of Alberta
KeywordsHospital Anxiety and Depression ScaleMental healthAnxietyPandemicMedicineCohortDepression (economics)Health careCohort studyCoronavirus disease 2019 (COVID-19)PsychiatryDisease

Abstract

fetched live from OpenAlex

We investigated the availability and use of workplace mental health (MH) supports during the COVID-19 pandemic in a Canadian cohort of healthcare workers (HCW) and measured anxiety and depression by the Hospital Anxiety and Depression Scale (HADS) completed at four contacts 2020-2022. Reports were available for 4400 HCW working with patients. Half the HCWs had a clinically significant HADS score at one or more contacts Access to MH supports increased during the pandemic, with 94% reporting access to some workplace support by 2022: 47% had made use of at least one support. 25% of those with high HADS scores used no support. Older women and men with depressive conditions were less likely to report use. Reported use of an Employee Assistance Program was associated with a reduction in HADS scores in the following months.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.354
Threshold uncertainty score0.944

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.042
GPT teacher head0.401
Teacher spread0.358 · 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 teacher head, 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

Citations1
Published2024
Admission routes3
Has abstractyes

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