Logic model for a train-the-trainer program ensuring alignment with recovery college principles and values
Bibliographic record
Abstract
Purpose Recovery colleges (RC) provide free courses on mental health, well-being and recovery. Training of RC trainers is a crucial aspect of ensuring fidelity to the RC, but to date, there are no documented experiences of train-the-trainer (TTT) programs and good practices for training RC trainers. This paper presents the logic model of the TTT program developed by the Health and Recovery Learning Center in Quebec. This paper aims to provide an example of how a TTT program can be designed. Design/methodology/approach An RC in Quebec, Canada, has designed and implemented a TTT program in collaboration with several partners in the health and education sectors. A logic model was used to ensure explicit links between the program components (inputs, activities and tools) and the intended results (outputs, outcomes and impact). Findings The TTT program is structured around a robust logic model in which all components are linked, ensuring alignment with RC principles and values framework. Three key stages are depicted: recruitment, training modules and continuous support for trainers. Specific tools were also developed to promote and support trainers’ competencies and courses co-design. Originality/value This paper adds to the literature on RC by presenting the first documented TTT program designed for RC trainers. It provides an overview of co-production practices and intersectoral collaboration contributing to the understanding of key elements to be included in the implementation of an RC.
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 | 0.016 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".