Stakeholder perspectives on proposed policies to improve distribution and retention of doctors in rural areas of Uttar Pradesh, India
Bibliographic record
Abstract
BACKGROUND: In India, the distribution and retention of biomedical doctors in public sector facilities in rural areas is an obstacle to improving access to health services. The Government of Uttar Pradesh is developing a comprehensive, ten-year Human Resources for Health (HRH) strategy, which includes policies to address rural distribution and retention of government doctors in Uttar Pradesh (UP). We undertook a stakeholder analysis to understand stakeholder positions on particular policies within the strategy, and to examine how stakeholder power and interests would shape the development and implementation of these proposed policies. This paper focuses on the results of the stakeholder analysis pertaining to rural distribution and retention of doctors in the government sector in UP. Our objectives are to 1) analyze stakeholder power in influencing the adoption of policies; 2) compare and analyze stakeholder positions on specific policies, including their perspectives on the conditions for successful policy adoption and implementation; and 3) explore the challenges with developing and implementing a coordinated, 'bundled' approach to strengthening rural distribution and retention of doctors. METHODS: We utilized three forms of data collection for this study - document review, in-depth interviews and focus group discussions. We conducted 17 interviews and three focus group discussions with key stakeholders between September and November 2019. RESULTS: We found that the adoption of a coordinated policy approach for rural retention and distribution of doctors is negatively impacted by governance challenges and fragmentation within and beyond the health sector. Respondents also noted that the opposition to certain policies by health worker associations created challenges for comprehensive policy development. Finally, respondents believed that even in the event of policy adoption, implementation remained severely hampered by several factors, including weak mechanisms of accountability and perceived corruption at local, district and state level. CONCLUSION: Building on the findings of this analysis, we propose several strategies for addressing the challenges in improving access to government doctors in rural areas of UP, including additional policies that address key concerns raised by stakeholders, and improved mechanisms for coordination, accountability and transparency.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".