Which Factors Influence the Use of Patient-Reported Outcome Measures in Dutch Physiotherapy Practice? A Cross-Sectional Study
Bibliographic record
Abstract
Purpose: Patient-reported outcome measures (PROMs) have the potential to enhance the quality of health care but, as a result of suboptimal implementation, it is unclear whether they fulfil this role in physiotherapy practice. This cross-sectional study aimed to identify the factors influencing PROM use in Dutch private physiotherapy practices. Method: A total of 444 physiotherapists completed a self-assessment questionnaire and uploaded the data from their electronic health record (EHR) systems to the national registry of outcome data. Univariate and multivariate ordinal logistic and linear regression analysis were used to identify the factors associated with self-reported PROM use and PROM use registered in the EHR systems, which were derived from the self-assessment questionnaire and from the data in the national registry, respectively. Five categories with nine independent variables were selected as potential factors for regression analysis. The similarity between self-reported and registered PROM use was verified. Results: On the basis of self-report and EHR report, we found that 21.6% and 29.8% of participants, respectively, used PROMs with more than 80% of their patients, and we identified the factors associated with PROM use. Conclusions: The factors associated with PROM use are EHR systems that support PROM use and more knowledge about PROM use. These findings can guide future strategies to enhance the use of PROMs in physiotherapy practice.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".