Operational challenges in the implementation of an anti-stigma campaign in rural Andhra Pradesh, India
Bibliographic record
Abstract
BACKGROUND: Despite of literature available on mental health-related stigma interventions, little is reported about the operational challenges faced during the planning, implementation and evaluation phases. METHODS: The Systematic Medical Appraisal, Referral and Treatment Mental Health Project was implemented in 42 villages of the West Godavari district in India. Andersen's Behavioural Model for Health Services Use was adopted to understand the factors influencing anti-stigma campaign delivery and the strategies identified to overcome these challenges. RESULTS: The challenges faced during the planning and implementation phase included distance and time taken for travel by the field staff, inadequate mental health services and infrastructure within communities, engagement of community with the field staff and community's poor mental health literacy and knowledge. Strategies used to overcome these challenges were regular engagement with community stakeholders, understanding mental health literacy levels and seeking inputs from the community regarding campaign design, organizing live drama shows at community's preferred time and place and screening of recorded drama video clips where lives shows were difficult. The evaluation phase posed challenges such as non-availability of key stakeholders and inadequate time and funding to evaluate the entire study population. CONCLUSION: The reported findings can help in planning and scaling up of the anti-stigma campaign in large trials in similar settings.
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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 | 0.020 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.006 |
| 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".