Barriers and facilitators of leaders' initiation of community self‐help groups for well‐being: A mixed methods study
Bibliographic record
Abstract
AIM: We explored the perceived barriers and facilitators faced by leaders when initiating self-help groups (SHGs) for emotional well-being using an exploratory sequential mixed-methods design that combined focus groups and an online survey. Leaders, educators, and technicians from supportive organizations participated in four focus groups (n = 22), and 46% (n = 30) of trainees from SHGs leadership training courses completed the survey. Thematic analysis of qualitative data, combined with descriptive and textual analyses of survey responses, revealed two overarching themes: learning to lead and leading in practice. Early success of SHGs was linked to leaders' self-motivation and targeted leadership training. Facilitators included shared task distribution, horizontal relationships, and active dialogue facilitated leadership, while barriers comprised perceived role overload and logistical burdens. Survey findings reinforced the importance of co-responsibility, key training elements, and external support, including meeting spaces, integration, and publicity. These results suggest that initiating and maintaining mutual SHGs for emotional well-being may require recognizing the central role of leaders. Targeted training, professional accompaniment, and a stable community network are essential supports.
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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 | 0.022 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".