Evaluating rehabilitation outcomes from the client's perspective by identifying the gap between current and preferred movement ability
Bibliographic record
Abstract
PURPOSE: Examine the theoretical construct of a gap between people's perceived current and preferred movement abilities and its potential for evaluating rehabilitation outcomes against clients' desired goals. METHOD: A cross-section of 311 community-dwelling adults completed a 24-item movement ability measure (MAM) and a visual analog movement scale. In a nonrandomized pre-post design, two subsets of that population completed the measures again after 2 weeks: 35 clients undergoing outpatient physical therapy and 34 in a comparison group who were not undergoing physical therapy. Scores on the MAM were analyzed using item response theory methods. RESULTS: The gap between current and preferred ability in the 311 adults represented one level difference on average out of six designated movement levels on both measures. Clients about to undergo physical therapy had gaps approximately twice the size of gaps in the 34-person comparison group on both measures (P < 0.001). Both the MAM and the movement scale showed a significantly narrower gap after 2 weeks for the group in physical therapy (P < 0.001) but no change for the comparison group. CONCLUSIONS: Assessing gaps between client-perceived current and preferred movement ability following intervention may help in the evaluation of rehabilitation outcomes from the client's perspective.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.001 | 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".