Advanced musculoskeletal physiotherapy practice in Ireland: A National Survey
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Since 2011, advanced practice physiotherapists (APPs) have triaged the care of patients awaiting orthopaedic and rheumatology consultant/specialist doctor appointments in Ireland. APP services have evolved across the major hospitals (n = 16) and, after 5 years, profiling and evaluation of APP services was warranted. The present study profiled the national musculoskeletal APP services, focusing on service, clinician and patient outcome factors. METHODS: An online survey of physiotherapists in the allocated APP posts (n = 25) explored: service organization; clinician profile and experience of the advanced role; and patient wait times and outcome measures. Descriptive statistics were used to analyse hospital- and clinician-specific data, and a content analysis was performed to explore APP experiences. RESULTS: A 68% (n = 17) response from 13 sites was achieved, whereby 20 whole-time APP posts existed in services led by 91 consultant doctors. Co-location of APP and consultant clinics at 11 sites facilitated joint medical-APP processes, with between-site differences in autonomy to screen referral letters, and arrange investigations, injections and surgery. Although 83% had postgraduate qualifications, APPs also availed themselves of informal role-specific training. Positive APP experiences related to learning opportunities and clinical support networks but experiences were consultant dependent, with further service developments and formal training required to manage workloads. APPs reported reduced wait times and most commonly chose to capture function/disability in future evaluations. CONCLUSIONS: Variances existed in the organizational design and operating of APP services. Although highly experienced and qualified, APPs welcomed additional formal training and support, due to the complex, more medical nature of APP roles. Further formal evaluation, capturing patient outcomes, is proposed.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it