Effective communication between healthcare professionals and deaf and hard-of-hearing patients : forum
Bibliographic record
Abstract
The aim of this project was to facilitate easy recognition and improve effective communication skills between health care professionals and deaf and hard-of-hearing patients. In order to determine if there was effective communication between healthcare professionals and deaf and hard-of-hearing patients, a questionnaire was administered to 40 patients at Lady Michaelis Community Health Centre identified as being deaf or hard-of-hearing – 20 patients collecting acute medicines and 20 patients collecting chronic medicines. Patients were asked whether they used sign language as a form of communication, whether they understood how to use their medication, whether they had any problems communicating with health care professionals and whether they had any preferences about how health care professionals communicated with them. The majority of patients collecting acute (17/20) and chronic medicines (15/20) reported problems communicating with health professionals. Although 17/20 patients collecting chronic medicines used sign language, only 11/20 patients collecting acute medicines used this method. It appeared that patients relied on the instructions written on the medicine containers or on translators who could communicate with them in an appropriate manner. In order to address this problem, two posters – one on how to identify a deaf person and one on how to talk to a deaf person – were created for health professionals. These can be easily duplicated and used in training programmes and as reminders.
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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".