Interventions aimed at improving healthcare and health education equity for adult d/Deaf patients: a systematic review
Bibliographic record
Abstract
BACKGROUND: d/Deaf people suffer from inequitable access to healthcare and health information. This 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 have been documented but not systematically analyzed. The purpose of this systematic review is to obtain a global overview of what we know about interventions aimed at improving health equity for d/Deaf people. 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 healthcare and health-related interventions for d/Deaf people following the PRISMA-equity guidelines. We focused on interventions aimed at achieving equitable care and equitable access to health information for d/Deaf people. RESULTS: Forty-six studies were identified and analyzed. Seven categories of interventions facing healthcare or health education inequities emerged: use of Sign Language (1), translation, validation and identification of clinical tools and scales (2), healthcare provider training program (3), development of adapted healthcare facilities (4), online interventions (5), education programs (6) and videos (7). Despite some methodological limitations or lack of data, these interventions seem relevant to improve equity of care and health education for d/Deaf people. CONCLUSION: Interventions that promote healthcare equity, health education amongst d/Deaf patients and healthcare provider awareness of communication barriers and cultural sensitivity show promise in achieving more equitable care for d/Deaf patients. Meaningful engagement of d/Deaf individuals in the conceptualization, implementation and evaluation of health-related interventions is imperative.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.007 | 0.006 |
| 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".