Retention and motivation of health workers in remote and rural areas in Cross River State, Nigeria: a discrete choice experiment
Bibliographic record
Abstract
BACKGROUND: Cross River State is making investments geared towards ensuring equitable distribution and improved retention of its frontline health workforce in remote and rural areas. This informed the conduct of a discrete choice experiment to determine the motivating factors supporting the retention of healthcare workers. METHODS: Study participants were 198 final year students of nursing, midwifery and community health and frontline health workers. Eight focus group discussions and 38 key informant interviews were conducted to obtain information about the dimensions of the work conditions that are important to frontline health workers when choosing to take up posting or stay in their rural work locations. RESULTS: Health workers are 2.7 times more likely to take up a rural posting or continue to stay in their present rural duty posts if they receive a salary increment. They are also four times more likely to take a rural job posting if a basic housing or a housing allowance is provided. CONCLUSION: Improving working conditions of frontline health workers in terms of adequate staff strength, good skills mix and equipment, etc., as well as improving opportunities for career advancement will support retention in rural health posts.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".