Moral injury and the myth of resilience: A qualitative exploration of cancer care provision in ontario throughout the COVID-19 pandemic
Bibliographic record
Abstract
The COVID-19 pandemic profoundly altered the provision, experience, and outcomes of cancer care around the globe. With untold pressures on healthcare systems as well as unprecedented disruptions to cancer care services, those working in cancer care were exposed to new and intensified morally challenging and potentially traumatic conditions. This study qualitatively explores those experiences in two hospitals located in different regions of Ontario, Canada. Through interviews and focus groups with 32 individuals we found that healthcare workers navigated and were impacted by a variety of ongoing potentially morally injurious events and circumstances. We identified a number of themes and subthemes detailing their experiences of moral distress and injury arising from: 1) participating in perceived patient harms through changes in care provision such as delays and cancellations, 2) bearing witness to patients navigating care alone, 3) experiencing collective trauma with patients, families, and colleagues, and 4) feeling betrayed by leadership and the organization for, at times, leaving them feeling unsupported, unheard, undervalued, and undermined. This study provides a novel contribution by delineating both the heterogeneous and collective nature of pandemic-related events and circumstances that may be contributing to the ongoing moral distress and injury of healthcare workers. It also serves to provoke a discussion of moral injury and the myth of resilience in light of both the organizational contributions (e.g. resilience narratives) to the experience of moral injury and the promise of systems approaches for prevention and mitigation. Organizational sanctuary is explored as a systems model for providing safe and supportive working environments.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.029 | 0.027 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| 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".