AB100. Screening for vision and hearing loss in patients with dementia: recommendations from interviews with sensory experts
Bibliographic record
Abstract
Background: Dementia is a syndrome that affects a person’s ability to understand and express information. The higher prevalence of vision and/or hearing losses among persons with dementia in long-term care (LTC) facilities interferes with the ability of nurses to provide optimal care because communication is compromised. Therefore, the detection and screening for sensory impairment is of the utmost importance in LTC facilities; however, there is currently no agreement among nursing professionals on how to best identify such losses for the purpose of further referral, and the need for a validated screening measure suitable for nurses in LTC facilities is clear. The present project aims to close this gap by investigating the screening recommendations of vision- and hearing-care professionals working with clients affected by dementia. Methods: Eleven experts in audiology, optometry, deafblindness, and technology participated in individual semi-structured interviews on the topic of tools and strategies that can be used to screen individuals with dementia for sensory loss. Interview transcripts were coded by two evaluators using verbal agreement and consensus building. Results: Three main themes emerged from the interviews with experts: barriers, facilitators, and strategies. Barriers to sensory screening were often mentioned, particularly impaired communication and lack of staff cooperation. Facilitators consisted uniquely of people, such as family members, intervenors, and nurses. Strategies for sensory screening in this population consisted of improving communication through repetition and encouragements; considerations based on familiarity; and inferring an impairment on the basis of patient behaviour. Few of our interviewees were knowledgeable on the topic of screening apps. Conclusions: Our findings, to be integrated with a similar environmental scan conducted among LTC nurses, can inform the administration of sensory impairment screening tests among a population with dementia in order to optimize 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 | 0.017 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".