An intervention to reduce stigma and improve management of depression, risk of suicide/self-harm and other significant emotional or medically unexplained complaints among adolescents living in urban slums: protocol for the ARTEMIS project
Bibliographic record
Abstract
BACKGROUND: There are around 250 million adolescents in India. Adolescents are vulnerable to common mental disorders with depression and self-harm accounting for a major share of the burden of death and disability in this age group. Around 20% of children and adolescents are diagnosed with/ or live with a disabling mental illness. A national survey has found that suicide is the third leading cause of death among adolescents in India. The authors hypothesise that an intervention involving an anti-stigma campaign co-created by adolescents themselves, and a mobile technology-based electronic decision support system will help reduce stigma, depression, and suicide risk and improve mental health for high-risk adolescents living in urban slums in India. METHODS: The intervention will be implemented as a cluster randomised control trial in 30 slum clusters in each of the cities of Vijayawada and New Delhi in India. Adolescents aged 10 to 19 years will be screened for depression and suicide ideation using the Patient Health Questionnaire (PHQ-9). Two evaluation cohorts will be derived-a high-risk cohort with an elevated PHQ-9 score ≥ 10 and/or a positive response (score ≥ 2) to the suicide risk question on the PHQ-9, and a non-high-risk cohort comprising an equal number of adolescents not at elevated risk based on these scores. DISCUSSION: The key elements that ARTEMIS will focus on are increasing awareness among adolescents and the slum community on these mental health conditions as well as strengthening the skills of existing primary healthcare workers and promoting task sharing. The findings from this study will provide evidence to governments about strategies with potential for addressing the gaps in providing care for adolescents living in urban slums and experiencing depression, other significant emotional or medically unexplained complaints or increased suicide risk/self-harm and should have relevance not only for India but also for other low- and middle-income countries. TRIAL STATUS: Protocol version - V7, 20 Dec 2021 Recruitment start date: tentatively after 15th July 2022 Recruitment end date: tentatively 14th July 2023 (1 year after the trial start date) TRIAL REGISTRATION: The trial has been registered in the Clinical Trial Registry India, which is included in the WHO list of Registries ( https://www.who.int/clinical-trials-registry-platform/network/primary-registries ) Reference No. CTRI/2022/02/040307 . Registered on 18 February 2022. The tentative start date of participant recruitment for the trial will begin after 15th July 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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.044 | 0.004 |
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".