Government social assistance programmes are failing to protect the health of low-income populations: evidence from the USA and Canada (2003–2014)
Bibliographic record
Abstract
BACKGROUND: Social policies that improve the availability and distribution of key socioeconomic resources such as income, wealth and employment are believed to present the most promising avenue for reducing health inequalities. The present study aims to estimate the effect of social assistance recipiency on the health of low-income earners in the USA and Canada. METHODS: Drawing on nationally representative survey data (National Health Interview Survey and the Canadian Community Health Survey), we employed propensity score matching to match recipients of social assistance to comparable sets of non-recipient 'controls'. Using a variety of matching algorithms, we estimated the treatment effect of social assistance recipiency on self-rated health, chronic conditions, hypertension, obesity, smoking, binge drinking and physical inactivity. RESULTS: After accounting for underlying differences in the demographic and socioeconomic characteristics of recipients and non-recipients, we found that social assistance recipiency was associated with worse health status or, at best, the absence of a clear health advantage. This finding was consistent across several different matching strategies and a diverse range of health outcomes. CONCLUSIONS: From a public health perspective, our findings suggest that interventions are warranted to improve the scope and generosity of existing social assistance programmes. This may include reversing welfare reforms implemented over the past several decades, increasing benefit levels and untethering benefit recipiency from stringent work conditionalities.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".