Advanced musculoskeletal physiotherapy practice in Ireland: A National Survey
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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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".