The social utility of community treatment orders: Applying Girard’s mimetic theory to community‐based mandated mental health care
Bibliographic record
Abstract
Serious mental illness (SMI) has long posed a dilemma to society. The use of community treatment orders (CTOs), a legal means by which to deliver mandated psychiatric treatment to individuals while they live in the community, is a contemporary technique for managing SMI. CTOs (or a similar legal mechanism) are used in every province in Canada and in many jurisdictions around the world in the care and management of clients with severe and persistent mental illness (most frequently schizophrenia) who have a history of treatment non-compliance and subsequent relapse. Although there is ongoing controversy around CTOs, their use continues to be on the rise. René Girard's mimetic theory, in which he posits the social utility of the scapegoat mechanism, may shed some light on how established cultural patterns contribute to contemporary responses to SMI: how culture depends on the reproduction of certain narratives, and how these act to shape the identity of those involved. The CTO specifically can be seen to act as a scapegoating mechanism, wherein, by singling out and controlling individuals who appear to threaten social order, social order is restored. This paper reviews Girard's theory, looks at how it has been applied to SMI, and then considers how it may illuminate the social role of the CTO. This examination may provide mental health nurses with insight into the constructed identities of their patients, as well as the role of mental health care within broader cultural narratives.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.066 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.005 | 0.006 |
| 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".