Technical Inefficiency of District Hospitals in Côte d'Ivoire: Measurement, Causes and Consequences
Bibliographic record
Abstract
The aim of this study is to estimate the level of inefficiency and to identify the causes and consequences of Cote d’Ivoire public hospitals inefficiency. To that effect, we are using the non-parametric Data Envelopment Analysis (DEA) and the double Bootstrap procedures to analyze the data. The analysis of data from the Ministry of Health in Cote d’Ivoire reveals that districts’ hospitals are not technically efficient. This situation has a negative impact on hospital output in the country. Thus, the health system is impacted by the inefficiency of districts’ hospitals in accommodating the demand of health care. That technical inefficiency remains dependent on environmental factors that constitute an impediment for some of the levers ((ratio of doctors per capita, malnutrition, average length of stay, geographical access, and correlation Tuberculosis / HIV) and others (number of doctors in medical staff) able to increase hospitals technical efficiency. The outcomes of this study reveal two main stakes: firstly, the need for improvement of hospitals productive efficiency and secondly, the need for a better planning and utilization of the resources allocated to the health sector. Providing adequate responses to these concerns is extremely important for the country’s ambition to establish a universal health insurance system and improve the quality of health care services.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".