Logic model for mental health interventions: the recovery College Model in Quebec, Canada
Bibliographic record
Abstract
The Recovery College (RC) model represents a worldwide innovation in health systems. First appeared in England and now established in five continents, the RC model proposes a mental health educational approach in the community, emphasizingco-production, co-learning, and equity between theoretical/clinic knowledge and experiential knowledge (1-3). All participants(individuals with or without mental health challenges, their relatives, mental health professionals, health and education service providers, citizens) have access to mental health, recovery, and well-being training(4). Both participants and trainers collectively learn and reflect on their mental health attitudes, behaviors, and practices. This article reports on the co-construction process followed by Quebec's RC team, the first to have developed a RC logic model. The logic model conception followed six steps/strategies: 1) Participant observations, 2) Analysis of administrative documents, 3) Informal interviews and meetings with stakeholders (trainers, health service managers, and partner organizations) to better understand the implicit assumptions of the intervention, 4) Review of the literature related to the recovery college model, 5) Co-construction of causal links between resources, activities, and outcomes, 6) Validation and synthesis of the logic model. Finally, the logic model was disseminated, highlighting the relationships between the strategic resources needed for the key activities of the intervention to produce the expected outcomes.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".