Can small steps result in big changes? Preliminary effectiveness of a community-based diabetes prevention program
Bibliographic record
Abstract
Efficacy trials use highly controlled designs under ideal conditions. Once demonstrated, interventions can be translated into the community and the real-world effectiveness is examined to optimize their potential public health impact. Small Steps for Big Changes (SSBC) is a diabetes prevention program that has been translated into the community and is currently being run out of the local YMCA. SSBC is a theory-based three-week program consisting of one-on-one exercise and diet counselling, and supervised exercise training. The purpose of this study was to evaluate the effectiveness of the community-based SSBC program in reducing a variety of diabetes risk factors six months post-intervention. Participants with prediabetes (N=90, 71% female, Mage = 42.7 ± 5.6 years) completed the following measures pre-, post- and six months post-program: self-report physical activity and food frequency measure, weight, waist circumference, and a six-minute walk test. From pre-program to six-months post-program: Weight decreased (?-3.37kg), waist circumference decreased (?-4.19cm), and six-minute walk distance increased (?+6.8%). There were significant improvements observed in self-reported physical activity and food frequency. The effects observed from pre- to the six-month post-program were small to moderate (Cohen's ds from .14 to .56). Preliminary evaluation suggests that the SSBC counselling program is effective at promoting diet and physical activity behaviours to help reduce their risk of developing type 2 diabetes. Continued evaluation is required to determine whether the community-based SSBC program produces changes in clinically relevant outcomes (i.e., A1C) one year following the three-week program.Acknowledgments: Diabetes Canada and Michael Smith Foundation for Health Research postdoctoral awards
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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 | 0.024 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".