Long-term impact of COVID-19 pandemic: Moral tensions, distress, and injuries of healthcare workers
Bibliographic record
Abstract
Given the longevity of the COVID-19 pandemic, it is important to address the perceptions and experiences associated with the progression of the pandemic. This narrative can inform future strategies aimed at mitigating moral distress, injury, and chronic stress that restores resilience and well-being of HCWs. In this context, a longitudinal survey design was undertaken to explore how health care workers are experiencing the COVID-19 pandemic over time. A qualitative design was employed to analyze the open ended survey responses using a thematic analysis approach. All physicians and staff at an academic health science centre in Toronto, Ontario, Canada were invited to participate in the survey. The majority of survey respondents were nurses and physicians, followed by researchers/scientists, administrative assistants, laboratory technicians, managers, social workers, occupational therapists, administrators, clerks and medical imaging technologists. The inductive analysis revealed three themes that contributed to moral tensions and injury: 1) experiencing stress and distress with staffing shortages, increased patient care needs, and visitor restrictions; 2) feeling devalued and invisible due to lack of support and inequities; and 3) polarizing anti- and pro-public health measures and incivility. Study findings highlight the spectrum, magnitude, and severity of the emotional, psychological, and physical stress leading to moral injury experienced by the healthcare workforce. Our findings also point to continued, renewed, and new efforts in enhancing both individual and collective moral resilience to mitigate current and prevent future moral tensions and injury.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| 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".