Learner Experience of an Online Co-Learning Model to Support Mental Health during the COVID-19 Pandemic: A Qualitative Study
Bibliographic record
Abstract
The COVID-19 pandemic has had a negative impact on the mental health of the population such as increased levels of anxiety, psychological distress, isolation, etc. Access to mental health services has been limited due to the "overflow" of demands. The Recovery College (RC) model, an education-based approach, has addressed this challenge and provided online well-being and mental health courses to at-risk populations. The RC model proposes a co-learning space in an adult education program where learners from diverse backgrounds collectively learn and empower themselves to better address psychological well-being and mental health issues. The aim of this study was to document the experience of learners who participated in online RC courses during the COVID-19 pandemic and the perceived impact of these courses on their mental health. A qualitative interpretative descriptive study design was employed, and Miles and Huberman's stepwise content analysis method was used to mine the data for themes. Fourteen structured online interviews were conducted with a sample representative of the diversity of learners. Five categories of themes emerged: (1) updating and validating your mental health knowledge, (2) taking care of yourself and your mental health, (3) improving and modifying your behaviors and practices, (4) changing how you look at yourself and others, and (5) interacting and connecting with others. Results suggest that online RC courses can be an effective strategy for supporting individual self-regulation and empowerment, breaking social isolation, and reducing the effects of stress in times of social confinement measures and limited access to care.
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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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".