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Record W4409256634 · doi:10.3390/diabetology6040028

Diabetes-Specific Quality of Life Changes Associated with a Digital Support Intervention: A Study of Adults with Type 1 Diabetes

2025· article· en· W4409256634 on OpenAlexafffund
Xiao-Qing Lu, Anthony T. Vesco, Tricia S. Tang

Bibliographic record

VenueDiabetology · 2025
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsUniversity of British Columbia
FundersMichael Smith Health Research BCLawson Foundation
KeywordsDiabetes mellitusIntervention (counseling)Type 2 diabetesMedicineQuality of life (healthcare)Type 1 diabetesGerontologyQuality (philosophy)EndocrinologyNursing

Abstract

fetched live from OpenAlex

Although digital platforms have gained popularity in the delivery of diabetes interventions, few models have focused on type 1 diabetes (T1D), offer different support delivery mechanisms, and involve peer and health professional-led support. TRIFECTA is a six-month multi-modal digital support intervention that includes a 24/7 peer texting group, an “ask-the-expert” web-based portal, and professional-led virtual group-based interactive sessions. This study examined diabetes-specific quality of life (DSQoL) changes following TRIFECTA. DSQoL was measured using Type 1 Diabetes and Life, a self-report survey that allows for subscale analysis in different age groups. Among 60 adults with type 1 diabetes, improvements were observed for overall diabetes-specific quality of life, primarily driven by the 26–45 years cohort. Subscale analysis found DSQoL improved for emotional experiences and daily activities for adults 26–45 years old, and social isolation improved for adults 46–60 years old.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.697

Codex and Gemma teacher scores by category

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

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 head0.050
GPT teacher head0.401
Teacher spread0.352 · 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
Published2025
Admission routes2
Has abstractyes

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