The Hidden Crisis: Understanding Potentially Morally Injurious Events Experienced by Healthcare Providers during COVID-19 in Canada
Bibliographic record
Abstract
BACKGROUND: Healthcare providers (HCPs) may be at elevated risk for moral injury due to increased exposure to potentially morally injurious events (PMIEs) throughout the COVID-19 pandemic. Identifying PMIEs experienced during the COVID-19 pandemic is a critical first step for understanding moral injury in HCPs. Accordingly, the purpose of the present study was to gain a deeper understanding of the work-related PMIEs experienced by HCPs in Canada during the pandemic. METHODS: Canadian HCPs completed an online survey between February and December 2021 about mental health and functioning, including demographics and the Moral Injury Outcome Scale (MIOS). We conducted a qualitative thematic analysis of PMIEs described extemporaneously by HCPs in the open-text field of the MIOS. RESULTS: = 124) HCPs were included in analysis. Eight PMIE-related themes were identified, comprising patients dying alone; provision of futile care; professional opinion being ignored; witnessing patient harm; bullying, violence and divided opinions; resources and personal protective equipment; increased workload and decreased staffing; and conflicting values. CONCLUSIONS: Understanding broad categories of PMIES experienced by Canadian HCPs during the COVID-19 pandemic provides an opportunity to enhance cultural competency surrounding their experiences which will aid the development of targeted prevention and intervention approaches.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".