Assessing the staffing needs for primary health care centers in Cross River State, Nigeria: a workload indicators of staffing needs study
Bibliographic record
Abstract
BACKGROUND: A major human resources for health challenge for Nigeria is ensuring the availability and retention of adequate competent health workers in the right mix to provide health care particularly at primary health care facilities in remote and rural communities. This study applied the Workload Indicators of Staffing Need (WISN) method to determine the numbers of nurses, midwives, community health officers (CHOs), community health extension workers (CHEWs), and junior community health extension workers (JCHEWs) required to cope with health care service delivery at primary health care facilities in Cross River State; compare workloads of different cadres at selected health facilities, and identify facilities with highest workload pressure. METHODS: Cross River State in Nigeria has 18 local governments, 196 wards, and an estimated population of over three million people. We used the WISN method to estimate the numbers of nurses/midwives, CHOs/CHEWs, and JCHEWs required to cope with the workload in the 196 ward-level primary health care facilities. FINDINGS: Basic services provided by nurses/midwives, and CHOs/CHEWs were typical of the primary health care level. They are antenatal care, routine immunization, child welfare clinic, family planning, treatment of minor ailments, assisted and normal deliveries, postnatal care, emergencies, care of tuberculosis patients, and referrals. Findings show that available nurses/midwives for the 196 PHC facilities were 79, and the calculated requirement was 209, WISN ratio of 0.4 and difference of - 130; the existing number of CHOs/CHEWs was 808, the calculated requirement was 1,258, WISN ratio of 0.6, with a difference of - 450; and the number of existing JCHEWs was 258, the calculated requirement was 203, WISN ratio of 1.3 with a difference of 55. Cross River State had only 40% of required nurses and midwives; and 60% of CHOs/ CHEWs needed to provide health services in the ward-level PHC facilities. CONCLUSION: The findings from this study indicated marked shortages of needed health workforce particularly nurses and midwives at the primary level of care; and overlap in some of the tasks performed by nurses/midwives, CHO/CHEWs, and JCHEWs.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".