MétaCan
Menu
Back to cohort
Record W4414614236 · doi:10.1002/ajcp.70022

Barriers and facilitators of leaders' initiation of community self‐help groups for well‐being: A mixed methods study

2025· article· en· W4414614236 on OpenAlexaff
Luis González‐de Paz, Alicia Alcaraz‐Rodríguez, Pablo Gálvez-Hernández, Cristina Conejo, Carmen Herranz

Bibliographic record

VenueAmerican Journal of Community Psychology · 2025
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsPublic Health Ontario
Fundersnot available
KeywordsFocus groupThematic analysisHealth psychologyExploratory researchQualitative researchTask (project management)Public health

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.435
Threshold uncertainty score0.819

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.211
GPT teacher head0.550
Teacher spread0.340 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueAmerican Journal of Community PsychologySame topicMental Health and Patient InvolvementFrench-language works237,207