Factors associated with effective coverage of child health services in Burkina Faso
Bibliographic record
Abstract
OBJECTIVE: To identify factors associated with both crude and effective health service coverage of under-fives in rural Burkina Faso. METHODS: In a cross-sectional study, 494 first-line health facilities, 7347 households and 12 497 under-fives were surveyed. Two sequential logistic random effects models were conducted to assess factors associated with crude and effective coverage. RESULTS: Of 614 children under-five with a reported illness episode, 427 (69.5%) received care at a health facility. Of those, 274 (64.1%) received care at a health facility providing at least the minimum threshold of service quality. We found that younger age, having a severe illness, shorter distance between household and health facility, and being from wealthier households were positively associated with crude coverage. In addition, low patient caseload and longer consultation had a positive association, while frequent facility supervisions had a negative association with effective coverage. Moreover, the nurse to clinical staff ratio at the health facility was positively associated with both crude and effective coverage. CONCLUSION: Our study found that crude coverage is associated with pre-disposing and enabling factors of health care access, while the availability of nurses is a strong predictor for both crude and effective coverage. This suggests that in the context of scarcity of resources, investing in human resources in health sector could be one of the priorities for decision-makers to ensure children in need not only access to healthcare but also good quality of care.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".