Test-Retest Reliability of a Measure of Independence in Everyday Activities: The ADL Profile
Bibliographic record
Abstract
BACKGROUND: Very few performance-based measures used in occupational therapy have established test-retest reliability coefficients. OBJECTIVES OF STUDY: This study presents the test-retest reliability of the task and operation scores of a performance-based measure of independence in everyday activities called the ADL Profile. METHODS: 20 adults with severe traumatic brain injury (mean age 28.4 years; SD 9.9) were tested on two occasions with the 17 tasks (personal care, home, and community) of the ADL Profile. Kappa coefficients were calculated on both task and operation scores (formulating goal, planning, executing, and goal attainment). FINDINGS: Test-retest reliability was moderate to almost perfect on task and operation scores of all 17 tasks. The three tasks with only moderate agreement were more novel and complex (e.g., making a budget) for the participants. RELEVANCE TO CLINICAL PRACTICE: Use of measures that are stable over time is essential for treatment planning and research. Repeat testing is crucial with clients that require long periods of treatment (acute care, rehabilitation, and community integration) and multiple measurements of ADL independence. LIMITATIONS: The small sample size is a limit of the study. RECOMMENDATIONS FOR FURTHER RESEARCH: Alternate versions of the three tasks with only moderate agreement would need to be developed and other psychometric properties established.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".