Important considerations in the assessment of seniors who are aging with cognitive or intellectual disabilities
Bibliographic record
Abstract
The primary objective of the present review is to summarize key findings on the assessment approachesand interviewing techniques that best meet the functional capacity of elderly individuals who are aging withcognitive and/or intellectual disabilities. Assessment techniques are not always relevant to the population ofseniors who are aging with cognitive impairment or intellectual disabilities, because these individuals are oftenunable to describe or communicate their needs effectively. This makes interviewing such clients problematic,not only for healthcare professionals, but also for everyone who is engaged in providing support and care toseniors who are aging with such disabilities. A structured literature search was conducted in PubMed, MedLineand CINAHL from 1990 until August 2017 using terms such as “elderly”, “cognitive impairment”, “intellectualimpairment” and other synonyms. A total of 64 articles were identified and further analyzed. Based on thecurrent body of literature, assessment of seniors who are aging with any degree of cognitive and/or intellectualimpairments is complex and there is no gold standard. However, there are several strategies that can be helpfulin clinical practice and research. Further research is needed on both cognitive and intellectual disabilities toestablish a sound evidence for description, screening for risk factors or undetected problems, setting rehabilitationgoals, and monitoring treatment progress.
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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 | 0.012 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".