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Record W4410943657 · doi:10.1177/17589983251345406

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

2025· article· en· W4410943657 on OpenAlexaff
Katia Fournier, Lily Li, Lisa Newington, Alexia Karantana, Gráinne Bourke, Ryan Trickett, Donna L. Kennedy

Bibliographic record

VenueHand Therapy · 2025
Typearticle
Languageen
FieldHealth Professions
TopicNursing Roles and Practices
Canadian institutionsLondon Health Sciences Centre
FundersNIHR Imperial Biomedical Research Centre
KeywordsMedicineStakeholderPhysical therapyPublic relations

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.776
Threshold uncertainty score0.779

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.195
GPT teacher head0.507
Teacher spread0.312 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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