The influence of welfare spending on national immunization outcomes: A scoping review
Bibliographic record
Abstract
Background: National policies influence the environments in which people live, but the ways in which these national policies influence people’s health are not well understood. Welfare spending is one national policy that may influence population health. While some research indicates higher levels of welfare investment may positively influence health, mixed findings contradict this conclusion. These mixed results examining the link between welfare policies and health may be better understood by investigating the relationship between welfare spending and preventative health interventions, such as immunization. Objective: This article’s purpose is to summarize the literature studying the relationship between national welfare spending and immunization outcomes. Design: This scoping review used the Joanna Briggs scoping review method. Data sources: The scoping review utilized scholarly databases and a focused gray literature search to find research articles that explored relationships between welfare spending and immunization outcomes. Review methods: Data was extracted from articles, including themes, aims, populations, years of study, methods, and findings. The articles’ themes were further analyzed with a word cloud and principal component analysis to determine which themes were more likely to coincide in the literature. Results: Seven articles were included in the review. Most of these articles did not address the relationship between welfare spending or policy and immunizations directly or with rigorous methods. Conclusions: Ultimately, the results of the scoping review suggest a lack of literature regarding the relationship between welfare spending and immunization outcomes. Further research is needed to understand the impacts of national welfare spending on immunization 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 | 0.021 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.018 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 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".