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Record W2465916586 · doi:10.1097/bcr.0000000000000364

Burn Rehabilitation Therapists Competency Tool—Version 2

2016· article· en· W2465916586 on OpenAlexaff
Ingrid Parry, Lisa K. Forbes, David Lorello, Lynne Benavides, Catherine Calvert, Shu-Chuan Hsu, Annick Chouinard, Matthew Godleski, Phala A. Helm, Radha Holavanahalli, Jennifer Kemp-Offenberg, Catherine E. Ruiz, Rachel Shon, Jeffrey C. Schneider, Melinda Shetler, Oscar E. Suman, Bernadette Nedelec

Bibliographic record

VenueJournal of Burn Care & Research · 2016
Typearticle
Languageen
FieldMedicine
TopicBurn Injury Management and Outcomes
Canadian institutionsMcGill UniversityHealth Sciences Centre
Fundersnot available
KeywordsMedicineRehabilitationDelphi methodOccupational therapyBurn centerCore competencyPhysical therapyNursingMedical emergencyPoison control

Abstract

fetched live from OpenAlex

The Burn Rehabilitation Therapist Competency Tool (BRTCT) was developed in 2011 to define core knowledge and skill sets that are central to the job performance of occupational and physical therapists working with burn patients during acute hospitalization and initial rehabilitation. It was the first national effort to provide standards that burn centers could use for the training and evaluation of a BRT performance. The American Burn Association Rehabilitation Committee recently expanded the tool to include long-term rehabilitation and outpatient care in order to more fully represent all of the stages of care in which patients with burn injury receive therapy. Thirty-six burn centers contributed competencies, 17 rehabilitation experts participated in a systematic Delphi questionnaire process, and eight representatives from seven additional burn centers validated the tool. The revised BRTCT, called the BRTCT-2, includes four new practice domains and 28 new competency statements. The expanded tool provides a common framework of standards for performance for occupational and physical therapists working with patients throughout the full spectrum of burn 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)0.002
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.489
Threshold uncertainty score0.916

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.036
GPT teacher head0.381
Teacher spread0.345 · 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 designObservational
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

Citations18
Published2016
Admission routes1
Has abstractyes

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