The Complete Health Improvement Program (CHIP) And Reduction of Chronic Disease Risk Factors in Canada
Bibliographic record
Abstract
PURPOSE: The short-term effectiveness of the nutrition-centred Complete Health Improvement Program (CHIP) lifestyle intervention for improving selected chronic disease risk factors was examined in the Canadian setting. METHODS: A total of 1003 people (aged 56.3 ± 12.1 years, 68% female) were self-selected to participate in one of 27 CHIP interventions hosted in community settings by Seventh-day Adventist churches throughout Canada, between 2005 and 2011. The program centred on the promotion of a whole-food, plant-based eating pattern, and daily physical activity was also encouraged. Biometric measures, including body mass index (BMI), blood pressure (BP), blood lipid profile, and fasting blood sugar (FBS), were determined at program entry and 30 days into the intervention. RESULTS: Over 30 days, significant overall reductions (P<0.001) were recorded in the participants' BMI (-3.1%), systolic BP (-7.3%), diastolic BP (-4.3%), total cholesterol ([TC] -11.3%), low-density lipoprotein cholesterol ([LDL-C] -12.9%), triglycerides ([TG] -8.2%), and FBS (-7.0%). Participants with the highest classifications of TC, LDL-C, TG, and FBS at program entry experienced approximately 20% reductions in these measures in 30 days. CONCLUSIONS: The CHIP intervention, which centres on a whole-food, plant-based eating pattern, can lead to rapid and meaningful reductions in chronic disease risk factors in the Canadian context.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".