Insight Revisited: Relationality and Psychiatric Treatment Decision-Making Capacity
Bibliographic record
Abstract
In this chapter, I explore the implications of relational theory for a highly charged site of administrative state ordering wherein persons subject to, or under scrutiny in light of criteria for, involuntary psychiatric hospitalization undergo assessment of their capacity to make decisions about the psychiatric treatments prescribed to them. While assessment of capacity to make treatment decisions (which I will call assessment of “treatment capacity”) is implicit in the act of obtaining legally valid consent to medical treatment in any setting,1 the institutional mechanisms that are, or are not, in place for testing and disputing this form of decisional capacity in psychiatric hospital settings merit focused scrutiny.2 I argue, drawing on a decision of the Supreme Court of Canada issued in 2003, Starson v. Swayze, that the time is ripe to advance a relational approach to this contested site of medico-legal activity.3
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.044 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".