Is Public Private Partnership an Effective Alternative to Government in the Provision of Primary Health Care? A Case Study in Odisha
Bibliographic record
Abstract
Existing public health services are inadequate to cater to the growing demands of quality health care. Public Private Partnership (PPP) has evolved over the last decade as a newer arrangement. This study aimed to understand the breadth and depth of services in primary health centres (PHC) under government (PHC-GOV), NGO (PHC-NGO) and corporate (PHC-COR) management in Kendrapara district of Odisha. One PHC from each model was selected at random. Compliance with Indian Public Health Standards (IPHS), programme performance of last one year and perception of end-users about quality of services were studied. The Government of India (GOI) prescribed IPHS checklist, a performance indicator matrix and a semi-structured interview schedule, respectively, were used for data collection. There were no significant differences in the breadth and depth of services across all three models of PHC management. Comprehensive primary health care including immunization services, health promotion, treatment of common ailments, malaria management and delivery services were almost non-existent in these facilities. PHC-GOV had better accessibility, infrastructure, behaviour of doctors and availability of medicines, whereas laboratory service was better in PHC-NGO and PHC-COR. Human resources and programme performance of last one year was grossly inadequate across all three models. There is no remarkable improvement in the quality of services provided by PPP models. It may not serve as a substitute to inadequate recruitment and retention of staff, erratic programme review and poor capacity building, for attainment of optimal outcomes.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".