The impact of social assistance programs on population health: a systematic review of research in high-income countries
Bibliographic record
Abstract
BACKGROUND: Socioeconomic disadvantage is a fundamental cause of morbidity and mortality. One of the most important ways that governments buffer the adverse consequences of socioeconomic disadvantage is through the provision of social assistance. We conducted a systematic review of research examining the health impact of social assistance programs in high-income countries. METHODS: We systematically searched Embase, Medline, ProQuest, Scopus, and Web of Science from inception to December 2017 for peer-reviewed studies published in English-language journals. We identified empirical patterns through a qualitative synthesis of the evidence. We also evaluated the empirical rigour of the selected literature. RESULTS: Seventeen studies met our inclusion criteria. Thirteen descriptive studies rated as weak (n = 7), moderate (n = 4), and strong (n = 2) found that social assistance is associated with adverse health outcomes and that social assistance recipients exhibit worse health outcomes relative to non-recipients. Four experimental and quasi-experimental studies, all rated as strong (n = 4), found that efforts to limit the receipt of social assistance or reduce its generosity (also known as welfare reform) were associated with adverse health trends. CONCLUSIONS: Evidence from the existing literature suggests that social assistance programs in high-income countries are failing to maintain the health of socioeconomically disadvantaged populations. These findings may in part reflect the influence of residual confounding due to unobserved characteristics that distinguish recipients from non-recipients. They may also indicate that the scope and generosity of existing programs are insufficient to offset the negative health consequences of severe socioeconomic disadvantage.
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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 | 0.015 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.016 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".