COVID-19 Outbreak: Understanding Moral-Distress Experiences Faced by Healthcare Workers in British Columbia, Canada
Bibliographic record
Abstract
Pandemic-management plans shift the care model from patient-centred to public-centred and increase the risk of healthcare workers (HCWs) experiencing moral distress (MD). This study aimed to understand HCWs' MD experiences during the COVID-19 pandemic and to identify HCWs' preferred coping strategies. Based on a qualitative research methodology, three surveys were distributed at different stages of the pandemic response in British Columbia (BC), Canada. The thematic analysis of the data revealed common MD themes: concerns about ability to serve patients and about the risks intrinsic to the pandemic. Additionally, it revealed that COVID-19 fatigue and collateral impact of COVID-19 were important ethical challenges faced by the HCWs who completed the surveys. These experiences caused stress, anxiety, increased/decreased empathy, sleep disturbances, and feelings of helplessness. Respondents identified self-care and support provided by colleagues, family members, or friends as their main MD coping mechanisms. To a lesser extent, they also used formal sources of support provided by their employer and identified additional strategies they would like their employers to implement (e.g., improved access to mental health and wellness resources). These results may help inform pandemic policies for the future.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.020 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".