Nursing and midwife staffing needs in maternity wards in Burkina Faso referral hospitals
Bibliographic record
Abstract
BACKGROUND: In 2006, Burkina Faso set up a policy to subsidize the cost of obstetric and neonatal emergency care. This policy has undoubtedly increased attendance at all levels of the health pyramid. The aim of this study was to measure the capacity of referral hospitals' maternity services to cope with the demand for health services after the implementation of this policy. METHODS: This study was conducted in three referral health centres (CMAs, CHRs, and CHUs). The CHU Yalgado Ouédraogo (tertiary level) and the CMA in Sector 30 (primary level) were selected as health facilities in the capital, along with the Kaya CHR (secondary level). At each health facility, the study included official maternity ward staff only. We combined the two occupational categories (nurses and midwives) because they perform the same activities in these health facilities. We used the WISN method recommended by WHO to assess the availability of nurses and midwives. RESULTS: Nurses and midwives represented 38% of staff at the University Hospital, 65% in the CHR and 80% in the CMA. The number of nurses and midwives needed for carrying out the activities in the maternity ward in the University Hospital and the CMA is greater than the current workforce, with WISN ratio of 0.68 and 0.79 respectively. In the CHR, the current workforce is greater than the number required (WISN ratio = 2). CONCLUSION: This study showed a shortage of nurses and midwives in two health facilities in Ouagadougou, which confirms that there is considerable demand. At the Kaya CHR, there is currently enough staff to handle the workload in the maternity ward, which may indicate a need to expand the analysis to other health facilities to determine whether a redistribution of health human resources is warranted.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".