MétaCan
Menu
Back to cohort
Record W4381337842 · doi:10.2337/db23-526-p

526-P: Increasing the Confidence of Health Care Professionals Treating Patients Living with Type 1 Diabetes through the Support-Pro Online Education Platform

2023· article· en· W4381337842 on OpenAlexaffabout
Alexandra Katz, RÉMI RABASA-LHORET, MERYEM K. TALBO, Asmaa Housni, LEE HILL, Amélie Roy‐Fleming, Sarah Haag, ANNE-SOPHIE BRAZEAU

Bibliographic record

VenueDiabetes · 2023
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsSte. Anne's Hospital
Fundersnot available
KeywordsMedicineModalitiesHealth professionalsConfidence intervalFamily medicineHealth careNursingInternal medicine

Abstract

fetched live from OpenAlex

Introduction: Support-Pro is a comprehensive online platform that was developed for healthcare professionals (HCP) and covers all technological, treatment and lifestyle needs for type 1 diabetes (T1D) management in short, practical modules. It was developed as studies have demonstrated that HCPs who work in healthcare not specific to T1D, find it difficult to keep up-to-date with new developments that could improve T1D care and management. However, specialized online resources, such as Support-Pro, could help rectify this problem. The objective of the study is to assess whether Support-Pro can increase HCPs confidence in treating and supporting people with T1D (PWT1D) and their satisfaction with the platform. Methods: Open-label, non-randomized, 3-month trial with 142 HCPs who had access to Support-Pro, and 66 who completed 3 months on the platform (NCT04859205). Results: On average, the 66 participants (years of practice: 14±10; 90% women; 71% Caucasian; 44% dietitians, 30% nurses, 23% pharmacists, 3% physician or resident physician) reported at 3 months, an average 10% improvement in their confidence (total score 58% to 68%; p<0.0001) and a median satisfaction score of 76%. HCPs most valued features that supported knowledge gain, such as downloadable PDF documents (72%) and case studies (44%). The platform was most used in first month of access (page views 1st month: 50 [5;135] vs. 3rd month: 0 [0;18]). Conclusions: HCPs who completed 3 months on Support-Pro platform reported increased confidence in treating PWT1D and high appreciation of learning content and modalities. This platform could improve the support and advice provided to PWT1D followed-up outside specialized clinics. Disclosure A.Katz: None. R.Rabasa-lhoret: Consultant; Dexcom, Inc., Abbott, Janssen Pharmaceuticals, Inc., Novo Nordisk Canada Inc., Sanofi, Lilly, Tandem Diabetes Care, Inc., Insulet Corporation. M.K.Talbo: None. A.Housni: None. L.Hill: None. A.Roy-fleming: None. S.Haag: Other Relationship; Omnipod. A.Brazeau: Other Relationship; Dexcom, Inc., Diabète québec, Ordre des diététistes nutritionnistes du Québec, Research Support; Canadian Institutes of Health Research, Fonds de recherche du Québec en Santé. Funding Strategy Patient-Oriented Research (JT1-157204); JDRF (4-SRA-2018-651-Q-R)

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.019
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0190.001

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 head0.057
GPT teacher head0.431
Teacher spread0.374 · 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.

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 routes2
Has abstractyes

Explore more

Same venueDiabetesSame topicHealth Literacy and Information AccessibilityFrench-language works237,207