Recommendations for successful sensory screening in older adults with dementia in long-term care: a qualitative environmental scan of Canadian specialists
Bibliographic record
Abstract
OBJECTIVES: This study aimed to identify screening tools, technologies and strategies that vision and hearing care specialists recommend to front-line healthcare professionals for the screening of older adults in long-term care homes who have dementia. SETTING: An environmental scan of healthcare professionals took place via telephone interviews between December 2015 and March 2016. All interviews were audio recorded, transcribed, proofed for accuracy, and their contents thematically analysed by two members of the research team. PARTICIPANTS: A convenience sample of 11 professionals from across Canada specialising in the fields of vision and hearing healthcare and technology for older adults with cognitive impairment were included in the study. OUTCOME MEASURES: As part of a larger mixed-methods project, this qualitative study used semistructured interviews and their subsequent content analysis. RESULTS: Following a two-step content analysis of interview data, coded citations were grouped into three main categories: (1) barriers, (2) facilitators and (3) tools and strategies that do or do not work for sensory screening of older adults with dementia. We report on the information offered by participants within each of these themes, along with a summary of tools and strategies that work for screening older adults with dementia. CONCLUSIONS: Recommendations from sensory specialists to nurses working in long-term care included the need for improved interprofessional communication and collaboration, as well as flexibility, additional time and strategic use of clinical intuition and ingenuity. These suggestions at times contradicted the realities of service provision or the need for standardised and validated measures.
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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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.018 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".