Causes of attrition among frontline health workers in rural areas of Bauchi and Cross River States of Nigeria
Bibliographic record
Abstract
BACKGROUND: The situation of frontline health workers in the rural areas of Bauchi and Cross River States has been classified as critical regarding the shortages due to attrition. This affects health service delivery and outcomes. METHODS: We targeted 402 participants, and 389 frontline health workers (nurses, midwives, nurse/midwives, community health officers and community health extension workers) responded. They were drawn from 42 public primary healthcare centers: 23 from Cross River and 19 from Bauchi States. Five focused-group discussions were conducted with 42 facilities in-charges to identify what they perceived as the main causes of attrition in the rural areas. RESULTS: Our findings indicate that the reasons that had potential to cause attrition of the frontline health workers were either voluntary or involuntary. Out of the 81 nurses in the study, 66 (81 percent) would voluntarily exit the workforce while 15 (19 percent) would leave involuntarily. From a total number of 81 nurses, midwives and nurse/midwives from the two states, 75% would exit due to resignations in search of better prospects in the urban areas. Ninety-nine percent of the community health worker's attrition had very low intentions of exit, and it would mainly be due to retirements and deaths. CONCLUSION: Implementation of tailor-made strategies that reflect their needs is imperative in the two states to reduce attrition among frontline health workers and improve health service outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".