CANDIDACY 2.0-UNRAVELING HEALTH INEQUITIES IN CHRONIC CONDITION CARE ACCESS
Bibliographic record
Abstract
Abstract The Inverse Care Law, positing that those most in need of healthcare are least likely to receive it, continues to describe persistent health inequities among older adults with chronic conditions. This study introduces Candidacy 2.0 (Chronic Condition (CC)), an innovative framework designed to unravel the complex mechanisms of these inequities in healthcare access for diverse older adult populations. Using Critical Interpretive Synthesis, we analyzed qualitative and mixed methods literature on rheumatoid arthritis experiences across various populations, including racial and ethnic minorities, LGBTQI+ individuals, and those with disabilities. Our key finding was the identification of a crucial eighth dimension: the “embodied relational self,” which transforms the framework into a powerful tool for understanding intersectional experiences of health inequity. Candidacy 2.0 (CC) offers a comprehensive understanding of how patients’ and care providers’ experiences are shaped by systemic injustices and social determinants of health. By integrating approaches like intersectionality, concordance, and recursivity, this model provides a new lens to conceptualize and address health disparities in chronic condition management for older adults. The framework’s significance lies in its potential to guide the development of culturally relevant health promotion and intervention efforts, offering concrete explanations for access challenges rooted in socially patterned influences. Our findings have substantial implications for enhancing health equity in geriatric healthcare delivery, informing policy, and driving further research. By implementing Candidacy 2.0 (CC), stakeholders can develop targeted interventions that address the unique healthcare needs of diverse older adults, working towards eliminating disparities across the life course.
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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 | 0.024 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.022 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".