Links between discrimination and cardiovascular health among socially stigmatized groups: A systematic review
Bibliographic record
Abstract
BACKGROUND: There is a high prevalence of cardiovascular disease across diverse groups in the U.S. population, and increasing research has identified stigma as a potential barrier to cardiovascular disease prevention and treatment. This systematic review examines evidence linking discrimination and cardiovascular health among socially stigmatized groups. STUDY DESIGN: Six databases were systematically reviewed from inception through February 2018 for studies with adult subjects, focusing on cardiovascular health indicators among social groups stigmatized because of their gender, race/ethnicity, age, body weight/obesity, or sexual orientation. The Newcastle-Ottawa Scale was used to evaluate the methodological quality and risk of bias for nonrandomized studies, and the Cochrane Collaboration 7-item domain for randomized controlled and experimental trials. RESULTS: The search identified 84 eligible studies published between 1984 and 2017. Studies retrieved were categorized according to demonstrated links between stigma and cardiovascular disease risk factors including blood pressure (n = 45), heart rate variability (n = 6), blood/saliva cardiovascular biomarkers (n = 18), as well as other indicators of cardiovascular health (n = 15). Based on the findings from included studies, 86% concluded that there was a significant relationship among stigma or discrimination and cardiovascular health indicators among socially stigmatized groups. However, there were varying degrees of evidence supporting these relationships, depending on the type of discrimination and cardiovascular health indicator. The current evidence implies an association between perceived discrimination and cardiovascular health. However, a majority of these studies are cross-sectional (73%) and focus on racial discrimination (79%), while using a wide variety of measurements to assess social discrimination and cardiovascular health. CONCLUSIONS: Future research should include longitudinal and randomized controlled trial designs, with larger and more diverse samples of individuals with stigmatized identities, using consistent measurement approaches to assess social discrimination and its relationship with cardiovascular health.
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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 | 0.008 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.009 | 0.010 |
| 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".