Agreement on the use of sensory screening techniques by nurses for older adults with cognitive impairment in long-term care: a mixed-methods consensus approach
Bibliographic record
Abstract
Objective Based on two scoping reviews and two environmental scans, this study aimed at reaching consensus on the most suitable sensory screening tools for use by nurses working in long-term care homes, for the purpose of developing and validating a toolkit. Setting A mixed-methods consensus study was conducted through two rounds of virtual electronic suitability rankings, followed by one online discussion group to resolve remaining disagreements. Participants A 12-member convenience panel of specialists from three countries with expertise in sensory and cognitive ageing provided the ranking data, of whom four participated in the online discussion. Outcome measures As part of a larger mixed-methods project, the consensus was used to rank 22 vision and 20 hearing screening tests for suitability, based on 10 categories from the Quebec User Evaluation of Satisfaction with Assistive Technology questionnaire. Panellists were asked to score each test by category, and their responses were converted to z-scores, pooled and ranked. Outliers in assessment distribution were then returned to the individual team members to adjust scoring towards consensus. Results In order of ranking, the top 4 vision screening tests were hand motion , counting fingers , confrontation visual fields and the HOT-V chart , whereas the top 4 hearing screening tests were the Hearing Handicap Inventory for the Elderly , the Whisper Test , the Measure of Severity of Hearing Loss and the Hyperacusis Questionnaire , respectively. Conclusions The final selection of vision screening tests relied on observable visual behaviours, such as visibility of tasks within the central or peripheral visual field, whereas three of the four hearing tests relied on subjective report. Next, feasibility will be tested by nurses using these tools in a long-term care setting with persons with various levels of cognitive impairment.
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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 | 0.536 | 0.540 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.020 | 0.009 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.008 | 0.016 |
| Research integrity | 0.004 | 0.003 |
| 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".