Using Outcome Measures in Daily Practice: Development and Evaluation of an Implementation Strategy for Physiotherapists in the Netherlands
Bibliographic record
Abstract
PURPOSE: To describe the development of an educational programme for physiotherapists in the Netherlands, two toolkits of measurement instruments, and the evaluation of an implementation strategy. METHOD: The study used a controlled pre- and post-measurement design. A tailored educational programme for the use of outcome measures was developed that consisted of four training sessions and two toolkits of measurement instruments. Of 366 invited physiotherapists, 265 followed the educational programme (response rate 72.4%), and 235 randomly chosen control physiotherapists did not (28% response rate). The outcomes measured were participants' general attitude toward measurement instruments, their ability to choose measurement instruments, their use of measurement instruments, the applicability of the educational programme, and the changes in physiotherapy practice achieved as a result of the programme. RESULTS: Consistent (not occasional) use of measurement instruments increased from 26% to 41% in the intervention group; in the control group, use remained almost the same (45% vs 48%). Difficulty in choosing an appropriate measurement instrument decreased from 3.5 to 2.7 on a 5-point Likert-type scale. Finally, 91% of respondents found the educational programme useful, and 82% reported that it changed their physiotherapy practice. CONCLUSIONS: The educational programme and toolkits were useful and had a positive effect on physiotherapists' ability to choose among many possible outcome measures.
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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 | 0.087 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| 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".