Co-design of “Baatcheet,” a peer-supported, web-based storytelling intervention for young people with common mental health problems in India
Bibliographic record
Abstract
Abstract Background Engaging with personal mental health stories has the potential to help people with mental health difficulties by normalizing distressing experiences, imparting coping strategies and building hope. However, evidence-based mental health storytelling platforms are scarce, especially for young people in low-resource settings. Objective This paper presents an account of the co-design of ‘Baatcheet’ (‘conversation’ in Hindi), a peer-supported, web-based storytelling intervention aimed at 16–24-year-olds with depression and anxiety in New Delhi, India. Methods Development comprised three stages: (1) establishing a logic model through consultations with a Young People’s Advisory Group ( N = 11) and a stakeholder reference group ( N = 20); (2) elaborating intervention guiding principles and components through focus group discussions and co-design workshops ( N = 42); and (3) user-testing of prototypes. Results The developmental process identified key stakeholder preferences for an online, youth-focused mental health storytelling intervention. Baatcheet uses an interactive storytelling website containing a repository of personal stories about young people’s experiences of depression and anxiety. This is offered alongside brief support from a peer. Conclusions There are few story-based interventions addressing depression and anxiety for young people, especially in low-resource settings. Baatcheet has the potential to deliver engaging, accessible and timely mental health support to young people. A pilot evaluation is underway.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".