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Record W1505971076 · doi:10.2196/resprot.2823

Development of the OnTrack Diabetes Program

2015· article· en· W1505971076 on OpenAlexvenueno aff
Mandy Cassimatis, David J. Kavanagh, Andrew P. Hills, Anthony C Smith, Paul Scuffham, Steven Edge, Jeremy Gibson, Christian A. Gericke

Bibliographic record

VenueJMIR Research Protocols · 2015
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Education
Canadian institutionsnot available
Fundersnot available
KeywordsDysphoriaType 2 diabetesMedicineSelf-managementDiabetes managementDiabetes mellitusComputer sciencePsychiatryAnxiety

Abstract

fetched live from OpenAlex

BACKGROUND: Type 2 diabetes affects an estimated 347 million people worldwide and often leads to serious complications including blindness, kidney disease, and limb amputation. Comorbid dysphoria is common and is an independent risk factor for poor glycaemic control. Professional support for diabetes self-management and dysphoria has limited availability and involves high costs, especially after regular hours, and in rural and remote areas. Web-based cognitive behavior therapy offers highly accessible, acceptable, and cost-effective support for people with diabetes. This paper describes the development of OnTrack Diabetes, a self-guided, Web-based program to promote improved physical and emotional self-management in people with Type 2 diabetes. OBJECTIVE: The objective of the study is to describe the development of the OnTrack Diabetes program, which is a self-guided, Web-based program aimed to promote euthymia and improved disease self-management in people with Type 2 diabetes. METHODS: Semistructured interviews with 12 general practitioners and 13 patients with Type 2 diabetes identified enablers of and barriers to effective diabetes self-management, requirements for additional support, and potential program elements. Existing resources and research data informed the development of content, and consultants from relevant disciplines provided feedback on draft segments and reviewed the program before release. Using a self-guided delivery format contained costs, in addition to adapting program features and modules from an existing OnTrack program. RESULTS: A separate paper describes the protocol for a randomized controlled trial to provide this required evaluation. CONCLUSIONS: Development of the OnTrack Diabetes program demonstrates strategies that help ensure that a program is acceptable to users. The next stages involve testing users' experiences and examining the program's effectiveness and cost-effectiveness in randomized controlled trials. TRIAL REGISTRATION: The Australian New Zealand Clinical Trials Registry (ACTRN): 12614001126606; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?ACTRN=12614001126606 (Archived by WebCite at http://www.webcitation.org/6U0Fh3vOj).

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)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0160.001

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 head0.438
GPT teacher head0.585
Teacher spread0.147 · 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 designNot applicable
Domainnot available
GenreProtocol

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

Citations10
Published2015
Admission routes1
Has abstractyes

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