Making the Case for Trauma-informed Supervision of Forensic Mental Health Trainees
Bibliographic record
Abstract
Forensic mental health professionals are often engaged in high stakes cases and work with trauma-affected evaluees. Supervision is a vitally important process to help forensic mental health trainees competently manage the complex nature of the work, both during training and well beyond. Working in forensic mental health contexts often exposes trainees to case material and experiences that may put them at risk for development of secondary traumatic stress (STS). This phenomenon is defined and differentiated from other related terms. The emerging literature about personal and professional risk factors and ways to mitigate STS are also outlined. A case is made that trauma-informed principles can be applied to supervision of forensic mental health trainees both to ensure the health and well-being of forensic professionals and to promote ethical practice.
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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 | 0.048 | 0.099 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.030 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.005 | 0.026 |
| Research integrity | 0.032 | 0.050 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".