Psychological distress among healthcare providers during the COVID-19 pandemic: patterns over time
Bibliographic record
Abstract
BACKGROUND: COVID-19 added to healthcare provider (HCP) distress, but patterns of change remain unclear. This study sought to determine if and how emotional distress varied among HCP between March 28, 2021 and December 1, 2023. METHODS: This longitudinal study was embedded within the 42-month prospective COVID-19 Cohort Study that recruited HCP from four Canadian provinces. Information was collected at enrollment, from annual exposure surveys, and vaccination and illness surveys. The 10-item Kessler Psychological Distress Scale (K10) was completed approximately every six months after March 28, 2021. Linear mixed effects models, specifically random intercept models, were generated to determine the impact of time on emotional distress while accounting for demographic and work-related factors. RESULTS: Between 2021 and 2023, the mean K10 score fell by 3.1 points, indicating decreased distress, but scores increased during periods of high levels of mitigation strategies against transmission of SARS-CoV-2, during winter months, and if taking antidepression, anti-anxiety or anti-insomnia medications. K10 scores were significantly lower for HCP who were male, older, had more children in their household, experienced prior COVID-19 illness(es), and for non-physician but regulated HCP versus nurses. A sensitivity analysis that included only those who had submitted at least five K10 surveys consisted of the factors in the full model excluding previous COVID-19 illness, occupation, and season, after adjustment. Models were also created for K10 anxiety and depression subscales. CONCLUSIONS: K10 scores decreased as the COVID-19 pandemic continued but increased during periods of high mitigation and the winter months. Personal and work-place factors also impacted HCP distress scores. Further research into best practices in distress identification and remediation is warranted to ensure future public health disasters are met with healthcare systems that are able to buffer HCP against short- and long-term mental health issues.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".