The impact of the COVID-19 pandemic on work-related mental disorder claims among healthcare workers : an interrupted time series analysis
Bibliographic record
Abstract
Healthcare workers (HCWs) have been shown to be impacted physically and mentally by the COVID-19 pandemic, attributed to stress and an increased workload. However, there is currently a gap in the literature regarding examination of mental disorders occurring in the workplace among HCWs. This study aims to address this gap by analyzing mental disorders claims that were accepted for workers’ compensation before and during the pandemic among HCWs and non-HCWs. Cases of mental disorder claims occurring among HCWs and non-HCWs were identified from accepted time-loss claims from the province of British Columbia’s workers’ compensation board (WorkSafeBC). Incidence rates were calculated using monthly estimates of the BC working population from Statistics Canada’s Labour Force Survey as the denominator. The periods assessed were January 2017 – December 2021. Changes in the incidence of mental disorder claims between healthcare workers and non-healthcare workers before and during the pandemic were estimated using controlled interrupted time series analysis. The results from the controlled interrupted time series analysis revealed no change in mental disorders claims among HCWs during the pandemic while a level change occurred for non-HCWs. This phenomenon cannot be attributed to a single factor. Potential explanations include the provincial healthcare system’s response to the pandemic, labour dynamics, as well as how WorkSafeBC addresses mental disorder needs among HCWs. Further research is needed to understand the long-term effects of mental disorders occurring in the HCWs population beyond the pandemic. [An errata to this thesis was made available on 2024-07-23.]
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".