Using formative research to inform a mental health intervention for adolescents living in Indian slums: the ARTEMIS study
Bibliographic record
Abstract
BACKGROUND: Adolescents are vulnerable to stressors because of the rapid physical and mental changes that they go through during this life period. Young people residing in slum communities experience additional stressors due to living conditions, financial stress, and limited access to healthcare and social support services. The Adolescents' Resilience and Treatment nEeds for Mental Health in Indian Slums (ARTEMIS) study, is testing an intervention intended to improve mental health outcomes for adolescents living in urban slums in India combining an anti-stigma campaign with a digital health intervention to identify and manage depression, self-harm/suicide risk or other significant emotional complaints. METHODS: In the formative phase, we developed tools and processes for the ARTEMIS intervention. The two intervention components (anti-stigma and digital health) were implemented in purposively selected slums from the two study sites of New Delhi and Vijayawada. A mixed methods formative evaluation was undertaken to improve the understanding of site-specific context, assess feasibility and acceptability of the two components and identify required improvements to be made in the intervention. In-depth interviews and focus groups with key stakeholders (adolescents, parents, community health workers, doctors, and peer leaders), along with quantitative data from the digital health platform, were analysed. RESULTS: The anti-stigma campaign methods and materials were found to be acceptable and received overall positive feedback from adolescents. A total of 2752 adolescents were screened using the PHQ9 embedded into a digital application, 133 (4.8%) of whom were identified as at high-risk of depression and/or suicide. 57% (n = 75) of those at high risk were diagnosed and treated by primary health care (PHC) doctors, who were guided by an electronic decision support tool based on WHO's mhGAP algorithm, built into the digital health application. CONCLUSION: The formative evaluation of the intervention strategy led to enhanced understanding of the context, acceptability, and feasibility of the intervention. Feedback from stakeholders helped to identify key areas for improvement in the intervention; strategies to improve implementation included engaging with parents, organising health camps in the sites and formation of peer groups. TRIAL REGISTRATION: The trial has been registered in the Clinical Trial Registry India, which is included in the WHO list of Registries, Reference number: CTRI/2022/02/040307. Registered 18 February 2022.
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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 | 0.064 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| 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".