Evaluating the Impact of Public Health Policies on Health Outcomes in Nigeria Healthcare System: A Scoping Review
Bibliographic record
Abstract
There are a number of challenges facing the Health System in Nigeria such as poor structure of facilities, low manpower, and limited funds. This is where public health policy comes in, as public health policy is essential in addressing these challenges and improving health outcomes. This scoping review aims to provide an in-depth overview of the existing literature on public health policies in Nigeria, and particularly their effects on health systems in terms of infectious disease surveillance, maternal and child health, as well as healthcare access. A comprehensive database search (such as in PubMed and Dimensions), and a grey literature search, were carried out to identify applicable studies on public health policies in Nigeria and health related issues. The search results were filtered and further screened based on inclusion and exclusion criteria, and 25 publications were included in the review. We retrieved 1535 publications from PubMed, Dimensions, websites and institutions. Following application of inclusion/exclusion criteria and the removal of one duplicate, 97 articles were retained. Thirty-one studies were excluded after title and abstract review, and seven were not retrieved in full-text. Following a full text review of 59 papers, 33 were excluded, resulting in 25 articles that were included in the review. The review reports on the progress made by Nigeria in the implementation of public health policies for responding to major health problems. Policies such as the National Health Act, the National Health Promotion Policy, and diseases control initiatives have been instrumental in driving health outcomes. However, challenges such as limited funding, poor infrastructure, and insufficient human resources remain. Better health outcomes require stronger policy execution, increased financing, and capacity building. This study gives a complete overview, identifying information gaps that will help inform future policy and research.
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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 | 0.022 | 0.069 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.018 | 0.017 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".