What's in a website? Determining evidence-based components of exercise programs for diabetes management
Bibliographic record
Abstract
Introduction: Lifestyle modification, including physical activity, is effective for preventing and managing diabetes; however, evidence-based interventions are essential for effective behaviour change. Although the internet is a popular hub for health information, website quality for diabetes exercise programs remains unclear. The purposes of this research were to evaluate websites providing exercise programs regarding a) behaviour change techniques (BCTs), b) theoretical domains, and c) technical quality of resources provided. Methods: A larger systematic grey-literature internet search was conducted to identify exercise programs for diabetes and obesity management in the Okanagan. For this sub-project, program descriptions were inductively coded for (1) Theoretical Domains Framework (TDF), (2) Behaviour Change Techniques Taxonomy (BCTTv1), and (3) technical quality of the websites using the Journal of the American Medical Association (JAMA) criteria. Results: The internet search identified 7 exercise programs for diabetes care in the Okanagan. 5/7 programs referred to the TDF and BCTTv1. 7/14 TDFs were referenced across the 5 program descriptions. TDFs mentioned in all 5 programs were behavioural regulation and social influences. 12/93 BCTs were addressed. Social support (unspecified), social support (practical), and goal setting (behaviour) were the most commonly mentioned BCTs. One website satisfied all 6 JAMA criteria, while the mean score was 4. Every website met the authorship, disclosure, and contact information criteria. Conclusion: A portion of the exercise programs utilized evidence-based interventions, however, coding specific frameworks was challenging. Future research should interview service providers to gain a more comprehensive understanding of BCTs and theoretical rationale employed in the programs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.032 | 0.156 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".