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Record W4366979534 · doi:10.56885/klem67899

Knowledge into Action: Preliminary Results of an Assessment of Clinicians’ Intention to Use Inlow’s 60-second Diabetic Foot Screen

2023· article· en· W4366979534 on OpenAlexaboutno aff
Virginie Blanchette, Janet L. Kuhnke, Alice T Wagenaar

Bibliographic record

VenueLimb Preservation Journal · 2023
Typearticle
Languageen
FieldMedicine
TopicDiabetic Foot Ulcer Assessment and Management
Canadian institutionsnot available
Fundersnot available
KeywordsDiabetic footMedicineFoot (prosody)Health careMedical educationPopulationHealth professionalsPodiatryAction (physics)NursingPsychologyDiabetes mellitusAlternative medicinePolitical science

Abstract

fetched live from OpenAlex

In 2022, Wounds Canada updated Inlow's 60-second Diabetic Foot Screen tool to increase its functionality and, ultimately, its ease of use in clinical practice. The new version was launched at a workshop held at the Diabetes Canada national conference in Calgary, Alberta in November 2022. As a part of continuing professional development (CPD) the workshop focused on the understanding of the importance and role of diabetic foot screening in the diabetes care setting, developing skills and knowledge about when and how to screen for diabetic foot disease and understanding how to implement the diabetic foot care pathway through use of the tool. CPD encompasses the multiple educational and developmental activities that healthcare and social service professionals undertake to maintain and enhance their knowledge, skills, performance and relationships in the provision of health care and social services. Thus, as part of the processes we assessed the impact of CPD activities on participants' intentions to use Inlow's 60-second Diabetic Foot Screen after the workshop. These preliminary results illustrate the potential of using the CPD-REACTION tool, a theoretically validated questionnaire, in CPD activities for Inlow's 60-second Diabetic Foot Screen. This can improve its implementation, its scaling, and ultimately its impact on clinical practice across all care settings and on population health.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.598
Threshold uncertainty score0.662

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.112
GPT teacher head0.418
Teacher spread0.307 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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