Moral Distress and Resilience in the Occupational Therapy Workplace
Bibliographic record
Abstract
Healthcare professionals are inherently vulnerable to moral distress due to their frequent work with persons who are suffering or in crisis, in combination with the strong empathic orientation that underpins the very act of care giving. When accompanied by high workloads, deficiencies in management practices such as low recognition, lack of work autonomy, and/or insufficient opportunity for growth and development, persons in caring professions are at an even higher risk of moral distress. There is evidence that professional resilience is effective in mitigating workplace stress. Successful individual-management of moral distress requires attention to the broader institutional conditions under which these difficulties arise. This paper presents findings from 79 occupational therapists in Alberta and Saskatchewan, Canada, who participated in a survey of moral distress and resilience. On a standardized measure of resiliency their scores fell at the lower end of normal. On a standardized measure of moral distress, the highest levels involved issues of: time to do the job properly, deteriorated quality of care, insensitive co-workers, and unrealistic expectations from others. Nearly 50% reported that they had considered leaving a position due to moral distress. The survey was carried out with the goal of developing a teaching module that included education about moral distress and recommendations for the enhancement of both individual resilience and the construction of resiliency-promoting work environments.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".