Psychiatrist and trainee moral injury during the organisational long COVID of Australian acute psychiatric inpatient services
Bibliographic record
Abstract
OBJECTIVE: This paper provides a commentary on the risk of moral injury amongst psychiatrists and trainees working in the acute psychiatric hospital sector, during the third winter of the COVID-19 pandemic. CONCLUSIONS: Moral injuries arise from observing, causing or failing to prevent adverse outcomes that transgress core ethical and moral values. Potentially, morally injurious events (PMIEs) are more prevalent and potent while demand on acute hospitals is heightened with the emergence of highly infectious SARS-CoV-2-Omicron subvariants (BA.4 and BA.5). Acute hospital inpatient services were already facing extraordinary stresses in the context of increasingly depleted infrastructure and staffing related to the pandemic. These stresses have a high potential to be morally injurious. It is essential to immediately fund additional staff and resources and address workplace health and safety, to seek to arrest a spiral of moral injury and burnout amongst psychiatrists and trainees. We discuss recommended support strategies.
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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 | 0.009 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.013 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".