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Record W4409073349 · doi:10.1093/jbcr/iraf019.026

26 Clinical Competencies for the Burn Speech-Language Pathologist: A Multidisciplinary Development and Consensus Study

2025· article· en· W4409073349 on OpenAlexaffabout
Nicola Clayton, H Regal, Tiffany Mohr, Lori Ann Arguello, Kathleen Kerr, Matthew Godleski

Bibliographic record

VenueJournal of Burn Care & Research · 2025
Typearticle
Languageen
FieldMedicine
TopicBurn Injury Management and Outcomes
Canadian institutionsHealth Sciences CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineMultidisciplinary approachMultidisciplinary teamSpeech-Language PathologyIntensive care medicinePathologyMedical physicsMedical educationPhysical therapyNursing

Abstract

fetched live from OpenAlex

Abstract Introduction Clinical competency guidelines provide a benchmark to promote the provision of standard care to ensure safe and optimal patient management. Whilst nationally agreed clinical competencies are available for other specialized areas of Speech Language Pathology practice, none currently exist for burn injury. Therefore, we aimed to develop a competency tool specific to the burns Speech Language Pathologist (SLP). Methods Spearheaded by the American Burn Association (ABA) Rehabilitation Committee, a group of recognised burn SLPs, Burn Certified Physical and Occupational Therapists and a Burn Physiatrist used a staged approach to (1) synthesize current practice guidelines from a number of national and international burn centers, (2) employ modified Delphi methodology, and subsequently (3) facilitate and participate in expert consensus meetings, to develop and refine a burn SLP competency tool. The previously published Burn Rehabilitation Therapists Competency Tool (Version 2), endorsed by the ABA, was used as a framework. Results Eighteen expert multidisciplinary burn clinicians across 14 medical centers, representing three countries (USA: eight states, Canada: three provinces, Australia: one state) contributed to the development and refinement of the Burns SLP competency tool. A steering committee of five SLPs and the burn physiatrist identified 103 competency statements across 15 core clinical domains relevant to the Burns SLP. These were presented in the Delphi Round 1; 18 participants voted and following a consensus meeting, the tool was refined with statements and domains revised, merged and/or new items created as indicated. Two further Delphi Rounds followed by consensus meetings are currently in process. The final tool will comprise multiple domains (each containing knowledge and application competency statements) tailored specifically to the SLP skill set within adult and pediatric burn injury populations, across the continuum of care from acute through to rehabilitation. The tool presents two tiered levels of expertise: Level 1 = minimum level of specialist skill required to manage a severe burn patient, Level 2 = expert level of specialist skill and recognized resource to other SLPs). Conclusions This competency tool is the first internationally accepted standard of care for burn SLPs and provides a guideline for performance in managing burn patients throughout the acute and rehabilitative care continuum. Applicability of Research to Practice Internationally agreed competencies are needed for the Burns SLP. Development of a clinical competency package through expert multidisciplinary consultation and consensus will guide training, ensure consistency of care provision and benchmarking across facilities with the mutual goal to optimize patient care and outcomes. Once completed, the competencies may serve as a key benchmark to establishing a burn therapy certification for SLPs. Funding for the Study N/A

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.201
metaresearch head score (Gemma)0.218
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.201
Threshold uncertainty score0.985

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2010.218
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0060.003
Science and technology studies0.0030.003
Scholarly communication0.0030.005
Open science0.0040.012
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.151
GPT teacher head0.494
Teacher spread0.344 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designQualitative
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

Citations0
Published2025
Admission routes2
Has abstractyes

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