Navigating Cognitive Screening and Service Delivery for Sensory Impairment in Occupational Therapy
Bibliographic record
Abstract
Background. Cognitive screening tools rely on vision and hearing. However, sensory impairments, alone or in combination, can hinder the accurate identification of cognitive difficulties. Purpose. We investigated how occupational therapists adapt the administration of cognitive screening tests to clients who present with vision and/or hearing impairments as well as their self-perceived satisfaction with comprehensive service delivery with this population. Method. An online survey of Canadian occupational therapists gathered cross-sectional data on their approaches in their practice. Results. Occupational therapists reported a range of environmental and person-level accommodations. However, the type and number of accommodations provided, as well as satisfaction with service delivery, did not vary by years of experience or work setting, regardless of the sensory group (hearing, vision, or both). More challenges were observed when screening the cognition of clients with dual sensory impairment, as indicated by fewer reported assessment modifications and lower self-satisfaction with service-delivery skills. Conclusion. The results of the current study highlight the need to develop standardized and effective strategies to enhance cognitive screening for individuals with sensory impairments, along with initiatives for education and training for occupational therapists.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".