Assembling a Toolkit to Measure Geriatric Rehabilitation Outcomes
Bibliographic record
Abstract
OBJECTIVE: To gather and assemble relevant patient-based outcome measures with emphasis placed on the older adults' level of functioning and activity performance. DESIGN: The study was conducted in two phases. First, a set of relevant measurement instruments was identified, and their was value analyzed according to general characteristics and metrologic criteria. Second, this "toolkit" was pretested on 22 older adults with respect to the burden of assessment and the quality of the data. RESULTS: The toolkit includes eight measurement instruments related to mobility, basic activities of daily living, independent living, leisure, physical functioning, psychologic functioning, social functioning, and caregiver status. Participants' acceptance of the toolkit was high, with all subjects completing the toolkit in two sessions (30-90 mins each). The leisure participation and satisfaction measure was the most difficult to complete. Distributional properties were adequate to ascertain variability between subjects, except for a ceiling effect found for the social functioning measure. CONCLUSION: Measurement tools that are used in combination are needed to optimize the applicability and utility of outcome results. The toolkit has the potential to become a valuable method for researchers and clinicians reporting geriatric rehabilitation outcomes.
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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 | 0.031 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".