Structural interventions that affect racial inequities and their impact on population health outcomes: a systematic review
Bibliographic record
Abstract
Abstract Structural racism is the historical and ongoing reinforcement of racism within society due to discriminatory systems and inequitable distribution of key resources. Racism, embedded within institutional structures, processes and values, perpetuates historical injustices and restricts access to structural factors that directly impact health, such as housing, education and employment. Due to the complex and pervasive nature of structural racism, interventions that act at the structural level, rather than the individual level, are necessary to improve racial health equity. This systematic review was conducted to evaluate the effects of structural-level interventions on determinants of health and health outcomes for racialized populations. A total of 28 articles are included in this review, analyzing interventions such as supplemental income programs, minimum wage policies, nutrition safeguard programs, immigration-related policies, and reproductive and family-based policies. Most studies were quasi-experimental or natural experiments. Findings of studies were largely mixed, although there were clear benefits to policies that improve socioeconomic status and opportunities, and demonstrable harms from policies that restrict access to abortion or immigration. Overall, research on the effects of structural-level interventions to address health inequities is lacking, and the evidence base would benefit from well-designed studies on upstream policy interventions that effect the structural determinants of health and health inequities and improve daily living conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.004 |
| 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 teacher head, 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".