Health care providers’ perceptions of burnout and moral distress during the COVID-19 pandemic: A qualitative study from Saskatchewan, Canada
Bibliographic record
Abstract
OBJECTIVES: This study sought to describe feelings and perceptions of burnout and moral distress experienced by health care providers in the Canadian province of Saskatchewan during the COVID-19 pandemic. METHODS: This study was part of a larger mixed methods project, and we here report on the qualitative results relating to burnout and moral distress experienced by medical doctors, registered nurses and respiratory therapists. We used an exploratory, qualitative descriptive design involving one-one-one interviews with 24 health care providers. Interview data were analysed using a reflexive thematic analysis approach. RESULTS: We identified three overarching themes each for health care provider burnout and moral distress. Interviews revealed that providers experienced burnout through (i) increased expectations and (ii) unfavourable work environments, which led most of them to recognise (iii) a need to step back. Regarding moral distress, key themes were: (i) a sense of compromised care, (ii) feelings of bumping heads with authorities and patient families, and (iii) seeing patients make difficult decisions. CONCLUSION: Our study found that medical doctors, registered nurses and respiratory therapists working during the COVID-19 pandemic experienced and continue to experience significant burnout and moral distress. This was often driven by both institution- and system-level factors. There is a need for sustained investment to build and support a motivated health care workforce to prepare for future pandemics and health emergencies.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.023 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".