Deaf and hard of hearing awareness training: A mentor‐led workshop
Bibliographic record
Abstract
BACKGROUND: People who are deaf and hard of hearing (DHH) comprise a significant and increasing proportion of the population. They face many barriers to accessing good health care and major communication challenges with health professionals. There is evidence that DHH awareness training for health professionals needs improvement but little information about how such training is incorporated into curricula. The research question we address is how to develop and deliver an effective workshop for students led by people who, by definition, have barriers to communication due to hearing loss and deafness. METHODS: Workshop development was initiated and led by a medical student as a course project, in collaboration with DHH people, other students, and university faculty in an iterative participatory educational design process, supported by a community-based organization that provides programs and services for DHH people. Development resulted in a pilot workshop suitable for all health professional students. RESULTS: Three workshops were attended by a total of 49 students from 10 different health disciplines. Workshops were highly rated. Thematic analysis of post-workshop reflections written by occupational therapy students showed learning in the domains of knowledge, skills (practical tips and techniques), and attitudes (assumptions, motivation, reflection). CONCLUSIONS: Partnership with a community organization makes it feasible for DHH people to design and facilitate workshops. The organization can provide the necessary environment, technology, and support, and identify people with lived experience to be workshop mentors. Workshops help make students more aware of the needs of DHH people and motivate them to provide better care.
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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 | 0.014 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".