Nurses and the Discursive Construction of Procedural Justice in Review Board Hearings
Bibliographic record
Abstract
There has been growing interest about procedural justice in mental health tribunals. A process considered procedurally just increases adherence to treatment, increases compliance with judicial decisions and allows efficient community reintegration. Yet, little is known about how procedural justice is carried out and the role of professionals in its implementation. Stemming from the results of a critical ethnography of the Ontario Review Board, in this article we examine how procedural justice materializes during Review Board hearings and the role of nurses in this materialization. We do so by leveraging Goffman’s work on total institutions and institutional ceremonies. Our findings suggest that nurses participate in activities that provide a perception of procedural justice, rather than serve their patients’ right to true procedural justice. We conclude by recommending that nurses engage in reflections about the distal effects of their clinical practice to broaden the possibilities for resistance within the forensic psychiatric system.
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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 | 0.075 | 0.124 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.038 | 0.134 |
| Scholarly communication | 0.020 | 0.011 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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".