Institutional Analysis of Integrated Treatment for Co-Occurring Mental Health, Substance Use and Gambling Problems in Ontario: A Case Study
Bibliographic record
Abstract
This dissertation explores the institutionalized response of the mental health and addiction sectors in Ontario to the pervasive demand for integrated services for people with concurrent disorders. Building on neo-institutional theory, I argue that despite the fact that different stakeholders on multiple levels—provincial governments, service providers, and clients—have called for the integration of treatment for concurrent disorders, this integrated treatment is implemented as a rationalized myth and adopted only ceremonially.\nThis is demonstrated through a case study of two treatment programs that provide services to populations with concurrent mental health and substance use problems and gambling problems. Both programs are organized as part of the Centre for Addiction and Mental Health in Toronto.\nMy findings address both macro- and micro-level foundations of the institutionalization of integrated practices. I have identified four key factors in the process of establishing integrated treatment for concurrent disorders as a standard practice. First, changes in public perceptions of mental health, substance use and gambling problems are associated with subsequent shifts in federal and provincial policies and mandates. Second, the need to conform to the public’s expectation for more cost-effective services has brought challenges in providing comprehensive client-centered care. These challenges are exacerbated by the increased reliance on technically-driven cost efficiency when planning treatment outcomes. Third, on the micro level, the endorsement of evidence-based practices is mutually related to internal structuring and specialization of care. Lastly, the institutionalization of integrated treatment is associated with individual involvement by social actors and their pursuit of personal and professional interests.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".