Vision and hearing impairment and occupational therapy education: Needs and current practice
Bibliographic record
Abstract
Introduction It is unclear what sensory impairment screening content should be included in the core-educational process for occupational therapists. The purpose of this study was to identify what content is currently being taught with regard to screening for vision and hearing loss, and to gather recommendations from specialists in this field of practice in order to formulate recommendations to improve professional entry-level occupational therapy curriculum content. Method Using a mixed-methods design, the two-phase study investigated the perceptions of five curriculum chairs, as well as 10 occupational therapists specializing in sensory rehabilitation. Results Curriculum chairs reported minimal course content with regard to training in the sensory domain, a dearth that was corroborated by specialists working with individuals affected by sensory loss. While vision-related topics were well covered, hearing-related information was sparser, and dual sensory impairment was mostly absent. Conclusion Occupational therapists are well positioned to play an essential role with the population living with sensory loss. However, most clinicians are not adequately prepared to practice with this clientele, and most expertise is gained after graduation. There is a need for stakeholders to discuss the minimal acceptable curriculum content needed to ensure that graduates are prepared to work in this growing area
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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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".