Components of cognitive competence predictive of occupational competence in persons with dementia: A Delphi study
Bibliographic record
Abstract
BACKGROUND: Within the area of dementia care, occupational therapists are asked to predict occupational competence in everyday living and often do so by assessing cognitive competence. Considering the cognitive changes that occur with dementia over time, the construct of cognitive competence is a key consideration. Still, a gap exists in the literature examining the relationship between cognitive competence and occupational competence. PURPOSE: This study developed a consensus among participating Canadian occupational therapists regarding the components of cognitive competence they considered essential to predict occupational competence in people with dementia. METHOD: A three-round Delphi study was completed with English- and French-speaking occupational therapists (n = 127; 116; 125) experienced in dementia care. FINDINGS: Ten cognitive components were identified as essential to predict occupational competence in individuals with dementia. IMPLICATIONS: The 10 identified components provide direction for assessment practices and education in dementia care and for development of measurement tools.
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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 | 0.015 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".