“My words matter”: perspectives on evaluation from people who access and work in recovery colleges
Bibliographic record
Abstract
Purpose The purpose of this co-produced research project was to conduct interviews with people working in, volunteering with and accessing Canadian recovery colleges (RCs) to explore their perspectives on what an evaluation strategy for RCs could look like. Design/methodology/approach This study used a participatory action research approach and involved semistructured interviews with 29 people involved with RCs across Canada. Findings In this paper, the authors share insights from participants about the purposes of RC evaluation; key elements of evaluation; and the most applicable and effective approaches to evaluation. Participants indicated that RC evaluations should use a personalized, humanistic and accessible approach. The findings suggest that evaluations can serve multiple purposes and have the potential to support both organizational and personal-recovery goals if they are developed with meaningful input from people who access and work in RCs. Practical implications The findings can be used to guide evaluations in which aspects that are most important to those involved in RCs could inform choices, decisions, priorities, developments and adaptations in RC evaluation processes and, ultimately, in programming. Originality/value A recent scoping review revealed that although coproduction is a central feature of the RC model, coproduction principles are rarely acknowledged in descriptions of how RC evaluation strategies are developed. Exploring coproduction processes in all aspects of the RC model, including evaluation, can further the mission of RCs, which is to create spaces where people can come together and engage in mutual capacity-building and collaboration.
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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 | 0.164 | 0.203 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.058 | 0.094 |
| Scholarly communication | 0.036 | 0.020 |
| Open science | 0.005 | 0.021 |
| Research integrity | 0.012 | 0.022 |
| Insufficient payload (model declined to judge) | 0.004 | 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".