Patterns of financial incentives in primary healthcare settings in Nigeria: implications for the productivity of frontline health workers
Bibliographic record
Abstract
OBJECTIVE: This study was designed to explore the patterns of financial incentives received by some frontline health workers (including nurses, midwives as well as community health workers in paid employment) and the implications for their productivity within rural settings in Nigeria. A cross-sectional quantitative design in two States in Nigeria was adopted. Structured interviews were conducted with 114 frontline health workers. Bivariate analysis and multivariate regression analysis were carried out to explore relationships between the satisfaction of frontline health workers with the financial incentives received and their productivity in rural settings as well as the extent of any such relationships. RESULTS: Bivariate analysis demonstrated a statistically significant relationship (P = 0.013) between satisfaction with incentives received by frontline health workers and their productivity in rural settings. When other predictors were controlled for within a multivariate regression model, those who received incentives and were satisfied with the incentives were about three times more likely to be more productive at work than those who were unsatisfied with incentives (AOR: 3.3; P = 0.009, 95% CI = 1.3-8.2). In conclusion, the determination of type and content of incentives should be done in consultation with all relevant stakeholders, including possibly a cross-section of health workers themselves.
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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 | 0.008 |
| 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 | 0.000 |
| 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".