SENSORY LOSS AND DEMENTIA: INSIGHTS INTO LONG-TERM CARE NURSES’ EXPERIENCES OF CARE AND ASSESSMENT
Bibliographic record
Abstract
More than half of long-term care (LTC) residents are reported to be living with dementia, affecting their ability to understand and express information, thereby having profound implications for effective interactions. This is further compounded by hearing and vision loss which also affects two thirds of residents. Persons living with dementia have identified the assessment of such sensory impairment, along with its treatment and care, as a key research priority. As part of a larger project aiming to develop a package of effective sensory screening tools to identify LTC residents with dementia in need of specialist referral, an environmental scan was conducted to capture the tools and strategies currently being used by front line staff in this setting. A purposive sample of 20 registered nurses and registered practical nurses was interviewed across 2 facilities in Ontario, Canada, and asked about: their experiences of working with persons who have dementia and sensory loss; how they identify which residents have sensory impairment; ways in which current screening procedures could be improved; and, key elements to include in a sensory screening package. Using a strength-based analytical framework, we highlight “pockets of excellence” in nurses’ practices of care and assessment. Results from a two-step qualitative content analysis reveal diverging institutional frameworks of practice, along with shared barriers and enablers to the care and identification of sensory loss in this population. We also discuss examples of effective and creative strategies used by nurses to conduct informal assessments and enable communication with older adults who have dementia.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 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".