Bibliographic record
Abstract
BACKGROUND: The high overlap of mental health and substance use problems in the Canadian health care system and the subsequent demand for more effective services for clients with these high-risk issues have stimulated the debate on their integrated treatment. Although the idea of integration has been endorsed by decision makers at both programs and system levels, little attention has been paid to factors that have facilitated this process. PURPOSE: In this article, the processes by which organizational texts, language, metaphors, and symbols have facilitated institutionalization of integrated treatment are identified and discussed. METHODOLOGY/APPROACH: Findings from a qualitative case study of 2 treatment programs that were part of a large, urban hospital in Ontario providing services for populations with concurrent disorders are presented. Data were collected using semistructured interviews with professionals and clients, analysis of policy and organizational documents, and nonparticipant observations. FINDINGS: Research evidence on comorbidity, government reports, and other organizational texts that were created and disseminated across the province has contributed to the dissemination of the concept of integration. Certain ideas might be successfully implemented when environments are conducive to change; such environmental catalysts include the status of professionals who support new discourse, the characteristics and importance of the problem being addressed, and the timing of implementation. The findings clearly demonstrate that the conditions of the wider institutional environment-the emergence of research evidence on comorbidity and the provincial health care reform, with its focus on rationalizing the existing health care system-supported the idea of integration. PRACTICE IMPLICATIONS: The ability to understand how discursive activities of program planners, clinicians, and policy makers contribute to making new ideas deeply embedded in organizational structures can become an important mechanism of effective decision-making activities when health managers attempt to promote new plans and strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.001 | 0.003 |
| 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".