Users’ involvement in mental health services: programme logic model of an innovative initiative in integrated care
Bibliographic record
Abstract
BACKGROUND: (Connecting Community organisations-Institutions-Users, CCIU), involving community- and institution-based mental health workers, carers and users, is an innovative normative integrated care group (group for shared values, culture and vision) established by the Canadian Mental Health Association-Montreal Branch. A programme evaluation approach was used to conduct a logic analysis of the CCIU in order to understand the relationships between its resources, activities and outcomes, build a common understanding and, allow for its replication. METHODS: Five steps were involved in the creation of a programme logic model. A non-exhaustive literature search for similar initiatives, a review of documents related to the CCIU process and direct observations led to the development of a first model. Then, following a participatory and reflexive process, this model was validated with CCIU participants. RESULTS: A comprehensive model and a simplified model were created. Participants' experiential knowledge and scientific knowledge helped to identify the essential components of the successful operation of the CCIU. CONCLUSIONS: The CCIU, with its eight essential components, including relations based on equality and mutual respect, corresponds to an essential step in normative integration and integrated care that lead to improved quality services.
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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 | 0.018 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".