Association between efficiency and quality of care of public healthcare facilities: Evidence from Pakistan
Bibliographic record
Abstract
BACKGROUND: Maternal and child health is an important component of the Sustainable Development Goals. Pakistan has one of the worst maternal and neonatal health outcomes in the world. This is despite significant health system investments across the country. AIMS: The objectives of this study are twofold. First, the study estimates the technical efficiency of the public healthcare facilities in Pakistan, defined as the number of obstetric deliveries compared to the number of medical specialists, nurses, and other health and non-health staff members. Second, the study evaluates the relationship between efficiency and quality of care; the latter is measured in terms of maternal and neonatal mortality. MATERIALS & METHODS: The data were taken from the Pakistan Health Facility Assessment Survey. Efficiency score was calculated for 843 public healthcare facilities, using Stochastic Frontier Analysis. We then used two-stage residual inclusion approach with bootstrapping to evaluate the relationship between efficiency and quality. RESULTS AND DISCUSSION: The average efficiency score was 0.48 (range: 0-1) and none of the public healthcare facilities were on the frontier, implying that efficiency gains can be made across the board. The relationship between efficiency and quality is found to be positive and statistically significant, that is, more efficient healthcare facilities also had lower rates of maternal and neonatal mortality. CONCLUSION: We conclude that more efficient public healthcare facilities also had lower mortality rates, probably due to better infrastructure and health system financing.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".