How Do Psychosocial Support Groups in North India Support Collective Action for Mental Health? A Qualitative Study Using a Caring Methodology
Bibliographic record
Abstract
ABSTRACT In resource‐poor settings in South Asia, there are many informal assets in communities that support mental health. Using participatory approaches and a ‘caring methodology’ we aimed to examine whether women's psychosocial support groups improved mental health knowledge, safe social spaces, and collective action. We also hoped to act collectively for mental health through the project and to reflexively consider how this methodology cared for participants and researchers. We conducted this community‐based qualitative study in 2016, across three sites in Dehradun district, Uttarakhand, Northern India. Data were collected through focus group discussions with women involved in support groups (N = 10, representing 59 women) and key informant interviews (N = 8), as well as field notes, journals, and reflexive discussions. We analysed data using thematic analysis. This research both researched care and provided mental health care. We found that support groups as well as caring methodologies led to increased mental health knowledge, safer social spaces, improved mental health and more equal gender relations. This methodology also supported women to act collectively to support each other and share their mental health knowledge with others. The caring methodology was constrained by stark asymmetries in literacy and educational status between researchers and participants.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".