The impact of COVID-19 on work-related mental health claims of healthcare workers in British Columbia: an interrupted time series analysis
Bibliographic record
Abstract
Introduction Healthcare workers (HCW) have been at the forefront of providing care since the COVID-19 pandemic. In addition to physical demands, HCW are also vulnerable to mental health conditions due to the nature of their work. As a result, absenteeism among HCW is inevitable. In Canada, mental disorders caused by a stressor at work results in a work-related claim provided it meets the criteria of a governing worker’s compensation agency. While the literature points to varying prevalence rates of mental health illnesses among HCW, it remains unknown how the COVID-19 pandemic affected the number of work-related mental health claims in this population. Objectives To help fill this gap in knowledge, we will conduct this study that aims to determine the impact of the COVID-19 pandemic on the number of work-related mental health claims among HCW. Methods We will utilize deidentified individual data from a worker’s compensation agency in all of British Columbia. Mental health claims will be identified using an indicator for mental health. Diagnoses for mental health conditions in these claims are ascertained by a psychologist or psychiatrist. Differences in the number of mental health claims between HCW and non-HCW before (January - February 2020) and after (March 2020 - December 2021) the pandemic will be estimated using interrupted time series analysis. Results The findings will inform disability case managers, healthcare providers, and employers the importance of identifying appropriate work accommodations, return to work programs and additional mental health supports for HCW under mental health claims. Healthcare unions in British Columbia can use the findings to advocate for better work accommodations and mental health support for HCW. Conclusions Further understanding the complications of long-term effects of COVID-19 on mental health of HCW will inform workforce planning and patient care. Disclosure of Interest None Declared
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".