Evaluating ADL Measures from an Occupational Therapy Perspective
Bibliographic record
Abstract
BACKGROUND: Measures reflecting occupational therapy's conceptualization of occupational performance support the profession's contribution to evidence-based practice and fiscal accountability. PURPOSE: This study compared measures of performance-based activities of daily living (ADL) with principles of occupational therapy practice and intended outcomes. METHODS: Using an action research study design, occupational therapists and researchers (N= 13) systematically clarified the clinical problem, identified occupational therapy principles inherent to the assessment of daily living activity via nominal group technique; defined the key principles as constructs; and reframed these constructs as a questionnaire against which 18 published standardized ADL measures were evaluated. FINDINGS: Participants identified six measures as most congruent with principles of occupational therapy practice: ADL Profile, Assessment of Motor and Process Skills, Functional Performance Measure, Rivermead ADL Assessment, Edmans ADL Index, and Melville-Nelson Self-Care Assessment. IMPLICATIONS: Findings guide occupational therapists' search and use of performance-based ADL measures that demonstrate the profession's distinct health care contribution.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.047 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".