Interventions aimed at improving health care equity for d/Deaf patients: a systematic review
Bibliographic record
Abstract
Abstract Background d/Deaf people suffer from inequitable access to care and health information, which results in worse health literacy and poorer mental and physical health compared to hearing populations. Various interventions aimed at improving health equity for d/Deaf people exist in the scientific literature but have to be systematically analysed. The purpose of this systematic literature review was to obtain a global overview of what we know about these interventions. Methods Medline Ovid SP, Embase, CINAHL EBSCO, PsycINFO Ovid SP, Central - Cochrane Library Wiley and Web of Science were searched for relevant studies on access to health care, interventions and health education for d/Deaf people following PRISMA-equity guidelines. The outcomes of interest were interventions aimed at achieving equitable care access to health information for d/Deaf people. Results Forty-eight studies were analysed. Four main categories of interventions emerged: 1) interventions addressing direct clinical care, 2) health technology-based interventions, 3) interventions focused on patient education, and 4) interventions focused on health care provider education. Among them, access to sign language interpretation or to culturally and linguistically adapted means of communication, use of communication media such as sign language video for the dissemination of medical information, and increasing use of technology (telemedicine, videoconferencing) were effective to strengthen equity in the care of d/Deaf people. Moreover, involving d/Deaf individuals in the conceptualization, creation, implementation and evaluation of interventions seemed to be imperative. Conclusions A multi-pronged approach, using a combination of interventions that improve health literacy among d/Deaf patients and promote health care providers' awareness of communication barriers and cultural sensitivity show promise in achieving more equitable care for d/Deaf patients, but more widespread research is needed. Key messages The scientific literature has identified and analysed many tools for improving health care equity for d/Deaf people, but their implementation is lacking. The involvement of d/Deaf people in research and implementation processes is crucial for the development of appropriate interventions.
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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 | 0.013 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".