Initial evidence of the effectiveness of a short, online Recovery College Model: a co-learning model to support mental health in the context of the Covid-19 pandemic
Bibliographic record
Abstract
Introduction. The Covid-19 pandemic (C-19) has a negative impact on the mental health of the general population and in particular, women, people with chronic physical illness or psychiatric conditions, students and health care providers. Targeting these needs, the Recovery College (RC) model offers a new and innovative approach based on co-learning and learner diversity. The model provides a co-learning space where at-risk populations and the general public learn together and collectively equip themselves to better address psychological well-being and mental health issues. Objective. The objective is to present the initial results of the RC co-learning model in a short online format to meet the pressing needs of mental health intervention in the C-19 context. Method. A pre-post research design with repeated measures was used. Results. Results suggest improved knowledge and use of tools to support mental health interventions, self-management strategies, anti-stigma attitudes, and protection against increased anxiety. Conclusion. This RC model allows people from all backgrounds to participate in an innovative co-learning model in which experiential knowledge is central to learning to stimulate reflection and change in attitudes and behaviors.
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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".