Optimizing Evaluation of Older Adults With Vision and/or Hearing Loss Using the interRAI Community Health Assessment and Deafblind Supplement
Bibliographic record
Abstract
Purpose: Service providers must identify and assess older adults who have concurrent vision and hearing loss, or dual sensory impairment (DSI). An assessment tool suitable for this purpose is the interRAI Community Health Assessment (CHA) and its Deafblind Supplement. This study's goal was to explore this assessment's administration process and to generate suggestions for assessors to help them optimize data collection. Methods: A social worker with experience working with adults who have sensory loss, who was also naïve to the interRAI CHA, administered the assessment with 200 older adults (65+) who had visual and/or hearing loss. The assessor evaluated the utility of the instrument for clinical purposes, focusing on sections relevant to identifying/characterizing adults with DSI. Results: Suggestions include the recommendation to ask additional questions regarding the person's functional abilities. This will help assessors deepen their understanding of the person's sensory status. Recommendations are also provided regarding sensory impairments and rehabilitation, in a general sense, to help assessors administer the interRAI CHA. Conclusions: Suggestions will help assessors to deepen their knowledge about sensory loss and comprehensively understand the assessment's questions, thereby allowing them to optimize the assessment process and increase their awareness of sensory loss in older adults.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".