Correctional officer experiences of moral distress, trauma-informed organizational practices, and structural stigma.
Bibliographic record
Abstract
The purpose of this pilot study was to explore moral distress (MD), organizational practices to reduce traumatic stress, and to elicit perspectives related to these challenges from professionals working in Canadian prisons and jails. This mixed-method study enlisted Canadian correctional agents (n = 77) for an online questionnaire including a measure of MD, a trauma-informed organizational assessment, and an open text box in which participants could share their perspectives in relation to MD and their organization’s efforts to mitigate traumatic stress. Three hypotheses were tested: (a) MD will be observed among Canadian correctional agents; (b) Actions to alleviate traumatic stress will correlate with lower MD levels; (c) Correctional agents belonging to equity-deserving groups will experience higher levels of MD. Qualitative data submitted by a subsample of the participants (n = 30) were inductively coded to describe their insights. Based on hierarchical regression analyses, the results indicated that a subgroup of correctional agents experience MD; those from groups facing discrimination may be at higher risk; and MD and trauma-informed organizational practices showed a negative relationship. Considering the risks MD is shown to pose to other professional groups, the advantages of trauma-informed initiatives to diminish harm and discrimination in correctional facilities, and the substantial distress endured by some correctional agents, future research should focus on effective systemic strategies to address the needs of equity-deserving correctional agents and to reduce MD among all correctional agents.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| 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".