Intersectionality and heart failure: what clinicians and researchers should know and do
Bibliographic record
Abstract
PURPOSE OF REVIEW: To review the application of intersectionality to heart failure. Intersectionality refers to the complex ways in which disenfranchisement and privilege intersect to reproduce and influence health and social outcomes. RECENT FINDINGS: Intersectionality challenges approaches that focus on a single or small number of socio-demographic characteristics, such as sex or age. Instead, approaches should take account of the nature and effects of a full range of socio-demographic factors linked to privilege, including: race and ethnicity, social class, income, age, gender identity, disability, geography, and immigration status. Although credible and well established across many fields - there is limited recognition of the effects of intersectionality in research into heart disease, including heart failure. This deficiency is important because heart failure remains a common and burdensome syndrome that requires complex pharmacological and nonpharmacological care and collaboration between health professionals, patients and caregivers during and at the end-of-life. SUMMARY: Approaches to heart failure clinical care should recognize more fully the nature and impact of patients' intersectionality- and how multiple factors interact and compound to influence patients and their caregivers' behaviours and health outcomes. Future research should explicate the ways in which multiple factors interact to influence health outcomes.
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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 | 0.010 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".