Measuring Technical Efficiency of Primary Health Care Providers: An Analysis from Ethiopia
Bibliographic record
Abstract
The following sections are included:The chapter reviews how a researcher might assess technical efficiency using descriptive analysis and data envelopment analysis of a defined set of health care providers with examples from an efficiency analysis conducted for Ethiopia’s primary health care (PHC) providers.The analysis demonstrated areas where possible inefficiencies are occurring within Ethiopia’s PHC system, but it does not address why such inefficiencies are happening or if the technically efficient health facilities are well functioning or if there are systematic breakdowns.Measuring technical efficiency is the first step in unpacking whether PHC facilities are operating at an acceptable level of efficiency and if not, why such inefficiencies are occurring and how to remedy them to improve performance.Efficiency of service delivery is influenced by both supply (influence of the production of health care) and demand (the use of health care by the population) factors, and the interaction between the two.In the case of Ethiopia, providers may need to pay more attention to individual preferences to care, perceptions of quality, and accessibility to increase utilization or demand for services. This might imply some shifts in effort by health providers towards outreach services and health promotion activities.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".