Can a Recovery College be implemented online? Multi-perspective case study documenting the process of adapting courses online
Bibliographic record
Abstract
Purpose This study aims to document the process of adapting Recovery College (RC) courses into an online format and assess whether online adaptation meets learners’ goals. The study describes the challenges encountered, the strategies deployed and the factors influencing implementation from the perspectives of four interested parties: coordination team, partners advisory committee, trainers and learners. Design/methodology/approach The study adopts a descriptive single-case study design. Several sources of data were collected: focus groups, implementation daybook, meeting minutes, interviews and satisfaction survey. Simple descriptive content analysis was used for all qualitative data, and simple descriptive statistical analyses were used for the online satisfaction survey with learners. Findings The results highlight challenges and strategies for adapting content and facilitation, as well as challenges and strategies for respecting RC key principles. Internal, organizational and technological factors have influenced the implementation. Most learners were satisfied with the courses attended and felt that it met their goals. Three overarching aspects of online implementation are discussed based on findings: training and supporting trainers, facilitation and pedagogical methods and ongoing monitoring and feedback to interested parties. Research limitations/implications Findings encourage further research to determine the extent to which online RC courses align with recommended strategies for reducing digital inequalities and implementing digital health interventions. Originality/value Few studies have focused on implementing RC online. This case study offers insights for organizations pursuing similar initiatives.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".