Integrating services for patients with mental and substance use disorders
Bibliographic record
Abstract
BACKGROUND: Over the past decade, practice standards have recommended that people suffering from both mental and substance use disorders receive integrated treatment. Yet, few institutions offer integrated services, and patients are too often turned away from psychiatric and addiction rehabilitation services. PURPOSE: The purpose of this study was to identify key factors in integrating services for patients with co-occurring disorders. METHODOLOGY: We conducted a process evaluation with the aim of identifying factors that enhance or impede service integration. First, we elaborated a sound conceptual framework of service integration. We then conducted in-depth case studies analysis using socioanthropological methods (interviews with managers and professionals, focus groups with patients, nonparticipant observation, and document analysis). We analyzed two contrasted forms of services integration, a joint venture and a strategic alliance, separately and then compared them. FINDINGS: The integrations achieved in the two cases were of different intensities. However, from our study, we were able to identify various levers and characteristics that affect the development of an integrated approach. Reflecting on the dynamics of these two cases, we formulated six propositions to identify what matters when integrating services for persons with mental and substance use disorders. PRACTICE IMPLICATIONS: The integration of services transcends debates on care models and must be focused on the patients' experience of care. The process should stimulate a learning experience that helps to align practices (normative integration) and to integrate teams and care. In this study, we identified a number of key conditions and levers for success.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".