Inefficiency Factors in Basic Health Care Supply in Côte d’Ivoire: A Comparative Econometric Analysis
Bibliographic record
Abstract
The achievement of universal health coverage has put Primary Health Care back at the center of policy orientations, particularly by identifying factors likely to improve the organization of peripheral facilities. However, this objective depends on the econometric methods used, especially for cross-sectional data and small sample sizes. This study aims to examine the sensitivity of the most usual estimation methods (Stochastic Frontier Analysis (SFA), Data Envelopment Analysis (DEA), DEA double bootstrap, Tobit, Truncated Standard Regression) for evaluating the scores and determinants of technical inefficiency of Primary Health Care Facilities (PHCF) in Côte d’Ivoire. Estimates show average technical efficiency scores of 94.13% for the DEA versus 89.61% for the SFA and 82.24% for the DEA double bootstrap. The results also indicate a proportion of determinants of technical inefficiency, in decreasing order of importance, with the DEA double bootstrap, the SFA, truncated regression and Tobit. This technical inefficiency can be improved in policies to promote basic health care by: increasing the proportion of nurses in the medical staff, the nurse/inhabitant ratio, the adult literacy rate by region, controlling the average capacity of the PHCFs, improving their geographical accessibility and reducing the rate of extreme poverty by health region.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".