Patterns of Distress and Supportive Resource Use by Healthcare Workers During the COVID-19 Pandemic
Bibliographic record
Abstract
Background/Objectives: Healthcare workers (HCW) have increased the risk of occupational stress injuries and adverse mental health outcomes, which were exacerbated during the COVID-19 pandemic. Understanding HCW psychological distress patterns and help-seeking behaviors can inform responsive resource development that may mitigate negative outcomes in future crises. This paper provides insights on monthly trends in HCW distress and support utilization at a large Canadian hospital over a 14-month period. Methods: As part of a hospital-wide wellness initiative during COVID-19, the STEADY program emailed monthly confidential wellness assessments to hospital staff from April 2020 to May 2021. The assessments included screens for burnout, anxiety, depression and posttraumatic stress, types of support accessed, and demographic information. Repeated cross-sectional data were summarized as monthly proportions and examined alongside longitudinal COVID-19 data. Results: A total of 2498 wellness assessments were submitted (M = ~168 monthly, range: 17–945). Overall, 67% of assessments had at least one positive screen for distress. Average positive screens were 44% for anxiety, 29% for depression, 31% for posttraumatic stress, and 53% for burnout. Despite high distress, most respondents used informal supports (e.g., family/friends), highlighting limited formal support use. Conclusions: HCWs experienced sustained high levels of psychological distress during the COVID-19 pandemic, with burnout remaining a predominant and persistent concern. The limited use of formal support services may indicate barriers to accessing these types of supports. Our findings underscore the need for accessible and acceptable mental health supports for HCW during prolonged crises.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".