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Record W4404687564 · doi:10.1080/20479700.2024.2429316

Pandemic-related organizational factors predicting physician anxiety and depression: Cross-sectional results from the COPING survey

2024· article· en· W4404687564 on OpenAlexaffabout
Mike Smyth, Michael P. Leiter, Kristen Bailey, Michael Wong, Jonathan G. Bailey

Bibliographic record

VenueInternational Journal of Healthcare Management · 2024
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare professionals’ stress and burnout
Canadian institutionsWestern UniversityAcadia UniversityDalhousie University
Fundersnot available
KeywordsCross-sectional studyAnxietyPandemicCoping (psychology)PsychologyDepression (economics)MedicineClinical psychologyFamily medicinePsychiatryCoronavirus disease 2019 (COVID-19)DiseaseInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

Physician depression and anxiety increased during the COVID-19 pandemic. This study aimed to determine what pandemic-related factors might be responsible for this increase, using a cross-sectional survey of Canadian physicians. Risk factors measured included the Pandemic Experiences and Perceptions Scale (PEPS) subscales (impact, adequacy, risk perception and worklife), COVID-19 preparedness, level of contact with COVID-19, and the number of provincial COVID-19 cases. In total, 309 completed the primary outcomes, with 20.1% experiencing symptoms of depression and 43.2% of anxiety. Structural equation modeling analysis demonstrated significant relationships between risk perception and areas of worklife to anxiety and to depression. The effect of worklife quality was partially mediated through reductions in risk perception. Areas of worklife explained 6% of the variance of risk perception. The explanatory variables in the model described 33% of the variability in anxiety and 28% of the variability in depression. Areas of work-life and risk perception were both significant contributors to depression and anxiety among physicians during early stages of the pandemic. To reduce symptoms, the aim of healthcare organizations should be to ensure adequate resources, reduce risk perception, improve communication and education regarding what is known about pandemics or crises.Trial registration: ClinicalTrials.gov identifier: NCT04379063.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.033
Threshold uncertainty score0.720

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.066
GPT teacher head0.428
Teacher spread0.362 · 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 routes2
Has abstractyes

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