Bibliographic record
Abstract
There is a current need to improve health care delivery to deaf and hearing-impaired persons. The author designed an educational workshop for medical students and others as an initial step to address this need. The workshop was offered electively during 1997 and 1998 to first-year and second-year medical students at Dalhousie University, Nova Scotia, Canada. The workshop involved a broad, multidisciplinary scope, may have been the first of its kind in Canada, and is still one of the few documented ways to approach medical education about deafness and hearing impairments. Attendees explored general information on hearing impairments, communication between the hearing-impaired patient and his or her physician, and multicultural, technological, and ethical aspects of caring for hearing-impaired patients. There was an initial questionnaire, group exercises, lectures, student interviews of volunteer deaf "patients," discussions, and a "hands-on" materials display. The workshop was a low-cost and easily reproducible method of educating medical students about hearing impairments. If found to be educationally effective through future research, this type of workshop may foster better care to deaf and hearing-impaired persons by inclusion into medical school and continuing education curricula.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.046 | 0.010 |
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".