Involuntary Detention Decision-Making, Criteria and Hearing Procedures: An Opportunity for Therapeutic Jurisprudence in Action
Bibliographic record
Abstract
This chapter examines criteria and procedures for fair, accurate and therapeutically constructive involuntary detention decision-making in the context of independent review by mental health review tribunals of clinical determinations about inpatient and outpatient detention. It also identifies key criteria for involuntary detention across a number of common law jurisdictions - England, the United States, New Zealand, Australia and Canada. A distinguishing feature of the United Kingdom legislation is that discharge of an involuntarily detained patient must take place if the tribunal is satisfied of a negative. An important factor in a patient feeling confidence in the course of a tribunal hearing is their perception first that they will be listened to and that the body deciding their case is unbiased. The challenge for review tribunals is to combine legal rigour in terms of fact-finding, avoiding the pretextual dishonesty, with informality, humanity and therapeutic awareness to ensure that the patient&s;s condition is not worsened by their experience appearing before a tribunal.
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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 | 0.019 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.026 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.013 |
| 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".