Cost of implementing the QualityRights programme in public hospitals in Gujarat providing mental healthcare
Bibliographic record
Abstract
Background & objectives: Investment in mental health is quite meagre worldwide, including in India. The costs of new interventions must be clarified to ensure the appropriate utilization of available resources. The government of Gujarat implemented QualityRights intervention at six public mental health hospitals. This study was aimed to project the costs of scaling up of the Gujarat QualityRights intervention to understand the additional resources needed for a broader implementation. Methods: Economic costs of the QualityRights intervention were calculated using an ingredients-based approach from the health systems' perspective. Major activities within the QualityRights intervention included assessment visits, meetings, training of trainers, provision of peer support and onsite training. Results: Total costs of implementing the QualityRights intervention varied from Indian Rupees (₹) 0.59 million to ₹ 2.59 million [1United States Dollars (US $) = ₹ 74.132] across six intervention sites at 2020 prices with 69-79 per cent of the cost being time cost. Scaling up the intervention to the entire State of Gujarat would require about two per cent increase in financial investment, or about 7.5 per cent increase in total cost including time costs over and above the costs of usual care for people with mental health conditions in public health facilities across the State. Interpretation & conclusions: The findings of this study suggest that human resources were the major cost contributor of the programme. Given the shortage of trained human resources in the mental health sector, appropriate planning during the scale-up phase of the QualityRights intervention is required to ensure all staff members receive the required training, and the treatment is not compromised during this training phase. As only about two per cent increase in financial cost can improve the quality of mental healthcare significantly, the State government can plan for its scale-up across the State.
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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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".