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Record W2508309232

Tissue Viability Leading Change competency framework: preliminary analysis of use

2016· article· en· W2508309232 on OpenAlexaff
Karen Ousey, John Stephenson, Becki Carter

Bibliographic record

VenueHuddersfield Research Portal (University of Huddersfield) · 2016
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsInstitute of Infection and Immunity
Fundersnot available
KeywordsMedical educationKnowledge managementProcess managementBusinessMedicineComputer science
DOInot available

Abstract

fetched live from OpenAlex

This paper presents preliminary analysis of clinicians use of the Tissue Viability Leading Change (TVLC) competency framework launched in November 2015. It is the first UK wide competency framework developed for staff working in a tissue viability service. Total downloads for the framework has been 331 to date. Overall initial feedback has been positive. The majority of respondents have accessed the framework for personal professional development. Respondents were asked to rate each of the TVLC competencies by frequency and ease of use. The first 5 competencies were utilised significantly more than competencies 6–10; the majority of respondents rated most competencies easy to use. Nine respondents (81.8%) reported that they were using, or intended to use, the TVLC as part of their appraisal process. Eleven respondents (84.6%) reported that they had used, or intended to use, the TVLC in validation processes as required by the Nursing and Midwifery Council (NMC).

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.957
Threshold uncertainty score0.612

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.3890.000

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 head0.200
GPT teacher head0.486
Teacher spread0.286 · 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.

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

Citations3
Published2016
Admission routes1
Has abstractyes

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