The mediating roles of workplace support and ethical work environment in associations between leadership and moral distress: a longitudinal study of Canadian health care workers during the COVID-19 pandemic
Bibliographic record
Abstract
Introduction: The COVID-19 pandemic has resulted in heightened moral distress among health care workers (HCWs) worldwide. Past research has shown that effective leadership may mitigate potential for the development of moral distress. However, no research to date has considered the mechanisms by which leadership might have an influence on moral distress. We sought to evaluate longitudinally whether Canadian HCWs' perceptions of workplace support and ethical work environment would mediate associations between leadership and moral distress. Methods: A total of 239 French- and English-speaking Canadian HCWs employed during the COVID-19 pandemic were recruited to participate in a longitudinal online survey. Participants completed measures of organizational and supervisory leadership at baseline and follow-up assessments of workplace support, perceptions of an ethical work environment, and moral distress. Results: Associations between both organizational and supervisory leadership and moral distress were fully mediated by workplace supports and perceptions of an ethical work environment. Discussion: To ensure HCW well-being and quality of care, it is important to ensure that HCWs are provided with adequate workplace supports, including manageable work hours, social support, and recognition for efforts, as well as an ethical workplace environment.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".