SCREENING FOR VISION AND HEARING LOSS IN OLDER ADULTS WITH DEMENTIA: A FEASIBILITY STUDY
Bibliographic record
Abstract
Abstract Sensory loss accounts for one of the most common chronic conditions among older adults, with hearing loss affecting half of adults aged over 65 years and vision loss almost one fifth of those aged 70 years and over. Together, dual sensory loss is found to be most prevalent in older adults with dementia. The highest prevalence is found in long-term care (LTC) settings. For this reason, we conducted a multi-stage study to identify the most effective vision and hearing screening tools for use with older adults living with dementia and to evaluate their feasibility of use by nurses working in LTC. We first conducted a comprehensive review of the literature, and supplemented this with an environmental scan of healthcare professionals and sensory specialists working with older adults who have dementia. Following this extensive review and consultative decision-making process, a package of vision and hearing screening tools was selected for use by nurses working in LTC. On-site training was provided by two experienced audiologists and optometrists, after which the feasibility of sensory screening by three nurses of 17 residents under their care was evaluated. We report on the six measures of hearing and seven measures of vision that were piloted for screening of older adults with dementia living in LTC, and on the findings for their feasibility of use by nurses working in this setting. Recommendations regarding the feasibility and reliability of screening for vision and hearing loss in older adults with dementia are discussed.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".