Impact of Self- and Peer Assessment on the Clinical Performance of Physiotherapists in Primary Care: A Cohort Study
Bibliographic record
Abstract
Purpose: This study evaluated the impact of a quality improvement programme based on self- and peer assessment to justify nationwide implementation. Method: Four professional networks of physiotherapists in The Netherlands (n = 379) participated in the programme, which consisted of two cycles of online self-assessment and peer assessment using video recordings of client communication and clinical records. Assessment was based on performance indicators that could be scored on a 5-point Likert scale, and online assessment was followed by face-to-face feedback discussions. After cycle 1, participants developed personal learning goals. These goals were analyzed thematically, and goal attainment was measured using a questionnaire. Improvement in performance was tested with multilevel regression analyses, comparing the self-assessment and peer-assessment scores in cycles 1 and 2. Results: In total, 364 (96%) of the participants were active in online self-assessment and peer assessment. However, online activities varied between cycle 1 and cycle 2 and between client communication and recordkeeping. Personal goals addressed client-centred communication (54%), recordkeeping (24%), performance and outcome measurement (15%), and other (7%). Goals were completely attained (29%), partly attained (64%), or not attained at all (7%). Self-assessment and peer-assessment scores improved significantly for both client communication (self-assessment = 11%; peer assessment = 8%) and recordkeeping (self-assessment = 7%; peer assessment = 4%). Conclusions: Self-assessment and peer assessment are effective in enhancing commitment to change and improving clinical performance. Nationwide implementation of the programme is justified. Future studies should address the impact on client 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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".