Assessing the Impact of Task-Shifting on Infant, Maternal and Child Health Outcomes in Rural Nigeria
Bibliographic record
Abstract
By using a longitudinal panel data approach, this study assesses how task-shifting affects maternal, infant, and child health outcomes in underdeveloped Nigeria. An analysis was done of data from administrative health records, national health surveys, and reports from the Federal Ministry of Health, UNICEF, and WHO. By means of a difference-in-differences (DiD) econometric technique, the study aims to estimate the impact of task-shifting by analyzing health results before and after implementation in institutions with and without the intervention. Key dependent variables are maternal mortality rate, infant mortality rate, under-five mortality rate, and immunization coverage. Independent variables include the degree of training given, the number of non-physician health care providers, and the status of task-shifting implementation. Regional fixed effects, facility characteristics, and socioeconomic variables are among the control variables. In facilities where task-shifting was applied, the outcomes show a statistically significant decrease in maternal and child death rates as well as an increase in immunization coverage. The validity of the results is confirmed by robustness checks including placebo tests and sensitivity analyses. Task-shifting is shown in the research to be a successful tactic for bettering health results in rural areas with limited resources. It recommends policies to strengthen training and support for non-physician healthcare workers and urges more widespread adoption of task-shifting initiatives to more effectively improve maternal and child health.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".