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Record W2030792587 · doi:10.1177/014572170102700110

Development of a Theory-Based Daily Activity Intervention for Individuals With Type 2 Diabetes

2001· article· en· W2030792587 on OpenAlexaff
Catrine Tudor‐Locke, Anita M. Myers, Nicole Rodger

Bibliographic record

VenueThe Diabetes Educator · 2001
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsSt Joseph's Health CentreUniversity of WaterlooWestern University
Fundersnot available
KeywordsIntervention (counseling)Psychological interventionType 2 diabetesPhysical activityVariety (cybernetics)Outcome (game theory)MedicinePsychologyPhysical therapyApplied psychologyDiabetes mellitusComputer scienceNursingMathematicsArtificial intelligence

Abstract

fetched live from OpenAlex

PURPOSE: This article describes a theory-driven approach to developing a physical activity intervention for sedentary individuals with type 2 diabetes. METHODS: Development of the intervention was based on 6 essential elements of program theory: problem definition, critical inputs, mediating processes, expected outcomes, extraneous factors, and implementation issues. Each element was formulated based on available literature and in collaboration with both intended service deliverers (diabetes educators) and recipients (sedentary persons with type 2 diabetes). RESULTS: Diabetes education requires a simple physical activity intervention template that is feasible, acceptable, and effective in a variety of settings. Successful programs are individualized, specific, flexible, and based on walking. Pedometers have potential as self-monitoring and feedback tools. The primary expected outcome is an increase in physical activity, specifically walking. Behavior modification and social support are critical to adoption and adherence. CONCLUSIONS: Theory-driven interventions specify what works for whom and under what conditions of delivery. The underlying theory guides the evaluation, refinement, and clinical replication of an intervention. Recruitment, delivery, and follow-up are real-world implementation issues.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.474
Threshold uncertainty score0.323

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.033
GPT teacher head0.322
Teacher spread0.290 · 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

Citations59
Published2001
Admission routes1
Has abstractyes

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