Efficiency Across Hospitals in Bangladesh: Results from Stochastic Frontier Analysis
Bibliographic record
Abstract
The health sector of Bangladesh achieved many of its goals. The sector, however, faces challenges. One major challenge is low efficiency. In a resourcepoor country, inefficiency leads to the waste of available resources widening the financing gap of the health sector. A technically efficient production unit produces a large amount of output with a given amount of inputs using a given state of technology. Technical efficiency of the district hospitals in Bangladesh is measured using the secondary source of data applying stochastic frontier analysis. Results show that the efficiency of some facilities is quite low and there is a mismatch of utilization rate and efficiency levels of the district hospitals. Measures like reducing absenteeism, increasing healthcare demand, and ensuring proper functioning of all inputs should be taken to enhance the efficiency and utilization of the district hospitals.
 Social Science Review, Vol. 37(2), Dec 2020 Page 145-159
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".