Competencies for advanced clinical practice (ACP) hand therapists in first-line management of closed hand fractures: Results of a United-Kingdom (UK) stakeholder consensus study
Bibliographic record
Abstract
Introduction: In the United Kingdom [UK], Advanced Clinical Practice (ACP) roles are being developed to improve access to high-quality patient care, where healthcare services are struggling to meet steadily increasing service demands. Increasingly, ACP hand therapists are assessing and treating acute closed hand fractures. However, the knowledge and skills required of these roles has not been identified or standardised. Methods: Consensus recommendations were developed from an expert panel of medical doctors and hand therapists using an electronic Delphi process. Participants were recruited from purposive and snowball sampling. Delphi questions were developed from a literature review and clinician survey and included rating of items open text responses. Consensus was defined as ≥75% agreement. Summary feedback was provided after each round. Results: There were 20 panellists (12 medical doctors and 8 hand therapists), of which 18 (90%) completed all rounds. 23 competencies were consistently identified as very important; there was less agreement on how to evidence these competencies. Conclusion: These findings can be used to develop ACP hand therapist roles and provide a framework to guide individual therapists to base their own learning and development. They underpin safe, efficient and costeffective patient care.
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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 | 0.100 | 0.152 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 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".