Systematic evaluation of the QualityRights programme in public mental health facilities in Gujarat, India
Bibliographic record
Abstract
BACKGROUND: Recognising the significant extent of poor-quality care and human rights issues in mental health, the World Health Organization launched the QualityRights initiative in 2013 as a practical tool for implementing human rights standards including the United Nations Convention on Rights of Persons with Disabilities (CRPD) at the ground level. AIMS: To describe the first large-scale implementation and evaluation of QualityRights as a scalable human rights-based approach in public mental health services in Gujarat, India. METHOD: This is a pragmatic trial involving implementation of QualityRights at six public mental health services chosen by the Government of Gujarat. For comparison, we identified three other public mental health services in Gujarat that did not receive the QualityRights intervention. RESULTS: Over a 12-month period, the quality of services provided by those services receiving the QualityRights intervention improved significantly. Staff in these services showed substantially improved attitudes towards service users (effect sizes 0.50-0.17), and service users reported feeling significantly more empowered (effect size 0.07) and satisfied with the services offered (effect size 0.09). Caregivers at the intervention services also reported a moderately reduced burden of care (effect size 0.15). CONCLUSIONS: To date, some countries are hesitant to reforming mental health services in line with the CRPD, which is partially attributable to a lack of knowledge and understanding about how this can be achieved. This evaluation shows that QualityRights can be effectively implemented even in resource-constrained settings and has a significant impact on the quality of mental health services.
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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 | 0.051 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".