Implementation of an Advanced Practice Physiotherapy Clinical Placement in Paediatric Rheumatology: A Case Report
Bibliographic record
Abstract
Purpose: To describe the implementation and reflections of an entry-level physiotherapy student clinical placement with an advanced physiotherapist practitioner (APP) in paediatric rheumatology. Method: A 7-week physiotherapy clinical placement consisting of 2 days/week supervised by an APP and 3 days/week supervised by a traditional physiotherapist was offered to a student in an entry-level Master of Science (MSc) Physiotherapy (PT) programme. Results: Student performance assessments included a learning contract and the Assessment of Clinical Performance tool. Final assessment in the traditional physiotherapy section aligned with expected MSc (PT) benchmarks. Applicable domains of the original Assessment of Clinical Performance (e.g., communication, professionalism) aligned with the advanced practice portion of the placement. Reflections included strengths of providing exposure and increased awareness of the APP role and providing a placement to practise core physiotherapy skills. Challenges included lack of standardization of APP roles contributing to difficulties in establishing delegation and expectations in the placement. Conclusions: Awareness of APP roles is limited, particularly among physiotherapy students. Developing student clinical placements in advanced practice roles may increase awareness while supporting clinical education opportunities in entry-level physiotherapy curricula. This case report offers insight into one APP clinical placement experience with recommendations for developing similar placement models.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".