Developing and Evaluating a Capacity-Building Intervention for Healthcare Providers to Improve Communication Skills and Awareness of Hard of Hearing and D/deaf Populations: Protocol for a Participative Action Research-Based Study
Bibliographic record
Abstract
Background: D/deaf and hard of hearing populations are at higher risk for experiencing physical and mental health problems compared to hearing populations. In addition, they commonly encounter barriers to accessing and benefiting from health services, which largely stem from challenges they face in communicating with healthcare providers. Healthcare providers commonly lack tailored communication skills in caring for D/deaf and hard of hearing populations, which lead to difficulties and dissatisfaction for both staff and D/deaf and hard of hearing communities. This research project aims to develop and evaluate a capacity-building intervention for healthcare providers with the goal of increasing their awareness of D/deaf and hard of hearing individuals' experiences with the healthcare system, their distinct needs, and improving their capacity to communicate effectively with this patient population. Methods: This research project features a participative action research design using qualitative and quantitative methods. Consistent with participative action research, the study will actively involve the target populations, key stakeholders and representative associations. The intervention will be developed and tested through iterative phases. The Integrated Model of Training Evaluation and Effectiveness will guide prospective evaluation of the intervention. The latter will involve qualitative and quantitative assessments in participants before and after the intervention and at 6-months follow-up. Discussion: Results will contribute to research aimed at decreasing barriers to accessing and benefiting from healthcare services for D/deaf and hard of hearing individuals. Findings will be presented to representative associations and political authorities, as well as disseminated at research conferences and in peer-reviewed journals.
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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 | 0.088 | 0.068 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.044 | 0.009 |
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".