Guaranteed income and health in the United States and Canada: a scoping review
Bibliographic record
Abstract
Although the economic impact of guaranteed income (GI) (recurring, unconditional, and unrestricted cash transfers intended to supplement the income of participants) is well studied, much less is known about how GI may affect health, especially in the context of high-income countries like the United States and Canada. We searched 5 electronic databases for terms related to "guaranteed income" and "cash transfer" through April 23, 2022. Among 5340 records originally identified, 25 met our inclusion criteria and represented 16 unique GI initiatives. Most included studies used a quantitative approach (n = 22; 88%), were published between 2000 and 2022 (n = 21; 84%), and were conducted in the United States (n = 15; 60%). Health outcomes included maternal and child health (eg, preterm births, breastfeeding initiation), healthcare utilization (eg, hospital admissions), mental health (eg, depression), physical health (eg, body mass index), and behavioral health (eg, substance use). Maternal, infant, and child health were the most highly represented health outcomes. Guaranteed-income initiatives generally had significant positive impacts on health outcomes, especially among the most vulnerable recipients. Data were absent on neighborhood-level health outcomes, chronic and infectious diseases, potential unintended consequences, and long-term impacts of GI on health. Studies on the impact of GI on health suggest GI has the potential to positively affect many, but not all, health outcomes. Rigorous assessment of health outcomes is still needed, and additional health outcomes should be considered in the design and evaluation of GI initiatives.
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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 | 0.011 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.028 | 0.051 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 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".