Cognitive Assessments for Older Adults: Which Ones are Used by Canadian Therapists and Why
Bibliographic record
Abstract
BACKGROUND: Occupational therapists routinely evaluate cognition in older adults, yet little is known about which assessments they use and for what purposes. PURPOSE: To examine the standardised and non-standardised assessments used by occupational therapists to evaluate cognition. METHOD: A random sample of 1042 Canadian occupational therapists completed the questionnaire by e-mail, post, or Internet website (n=247, response rate: 24.5%). RESULTS: Respondents reported using 75 standardised and non-standardised measures. The assessments were grouped according to theoretical approach: bottom-up (assessment of cognitive components), top-down (assessment of function) and combined (either of above, plus interview). Theoretical approaches were used similarly across regions, despite differences in reporting of particular assessments. Therapists used more bottom-up assessments that were standardised, identified deficits, and easy to administer. They used more top-down assessments that were non-standardised, predicted function, and fit with their theoretical approach. CONCLUSION: It is recommended that standardised top-down assessments be developed to support evidence-based occupational therapy.
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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 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".