Addressing Moral Distress During the COVID‐19 Pandemic: Insights About Future Directions From Canadian Ethicists and Healthcare Leaders
Bibliographic record
Abstract
Moral distress increased among healthcare workers during the first three years of the COVID-19 pandemic. This qualitative descriptive study explored the experiences of thirteen healthcare professionals with expertise in supporting healthcare workers experiencing moral distress within Canadian healthcare systems during this time. Participants reported multiple factors driving moral distress, such as resource scarcity (e.g., staffing shortages), policies (e.g., vaccination), and sociopolitical issues (e.g., diminishing support for healthcare workers). A range of interventions was employed to address moral distress, including: education, debriefing, consultation, mentorship, and general wellness programs. A strong knowledge of moral distress and counselling skills were both cited as necessary tools for individuals facilitating moral distress interventions. Values central to experiences of moral distress (e.g., transparency, accountability, respect, care) were identified, and participants described factors that could support organizational change to align with these values to better address moral distress (e.g., transparent communication, capacity-building). Finally, participants called for societal and political support for resource allocation to healthcare systems to ensure system sustainability and the ability of healthcare professionals to provide ethically sound care to all members of society.
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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 | 0.028 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.047 | 0.032 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 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".