Mapping the concept of vulnerability related to health care disparities: a scoping review
Bibliographic record
Abstract
BACKGROUND: The aim of this paper is to share the results of a scoping review that examined the relationship between health care disparities and the multiplicity of vulnerability factors that are often clustered together. METHODS: The conceptual framework used was an innovative dynamic model that we developed to analyze the co-existence of multiple vulnerability factors (multi-vulnerability) related to the phenomenon of the 'Inverse Care Law'. A total of 759 candidate references were identified through a literature search, of which 23 publications were deemed relevant to our scoping review. RESULTS: The review confirmed our hypothesis of a direct correlation between co-existing vulnerability factors and health care disparities. Several gaps in the literature were identified, such as a lack of research on vulnerable populations' perception of their own vulnerability and on multimorbidity and immigrant status as aspects of vulnerability. CONCLUSIONS: Future research addressing the revealed gaps would help foster primary care interventions that are responsive to the needs of vulnerable people and, eventually, contribute to the reduction of health care disparities in society.
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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 | 0.027 | 0.104 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.037 | 0.029 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| 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 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".