An inquiry into the Enablers of ‘Human Response Capacity’ in Individuals and Communities during instances of Mental Distress in India
Bibliographic record
Abstract
Mental health challenges affect a significant number of people globally. Technical solutions promote cure and healing. Parallelly, inclusion of the affected individuals and neighbourhoods in the solution is critical for addressing mental health stigma, utilising community competencies, building ownership and promoting sustainable solutions. Human capacity for response theory and the SALT (Support / Appreciate / Listen / Team) approach offers a framework to understand and utilise the inherent strengths of people. Volunteers are a useful resource, who offer their time and skills to address challenges. This research attempts to understand the coping mechanisms of communities facing mental health challenges, the use of the SALT approach by organisations to promote coping in communities, and implications of the SALT approach for volunteers. Case study method and thematic analysis was used to study three cases of 1) communities affected by drug use in Aizawl 2) suicides in Pune and 3) floods in Kochi, in India. The study reveals a nuanced response of stigma, ownership, connections for healing, and the role of spirituality by affected people; role of SALT in promoting acknowledgement, human connectedness, healing, and motivation to support others in similar distress; and the need to understand the motivation of volunteers and invest in their training and self-care, for sustained volunteering. This study illuminates the potential of communities to be a part of the solution, need for listening and community building - alongside technical support - by organisations, and importance of investing in capacity building of volunteers.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.017 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| 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".