A Community-Based, Environmental Chronic Disease Prevention Intervention to Improve Healthy Eating Psychosocial Factors and Behaviors in Indigenous Populations in the Canadian Arctic
Bibliographic record
Abstract
Diet-related chronic diseases are highly prevalent among indigenous populations in the Canadian Arctic. A community-based, multi-institutional nutritional and lifestyle intervention-Healthy Foods North-was implemented to improve food-related psychosocial factors and behaviors among Inuit and Inuvialuit in four intervention communities (with two comparison communities) in Nunavut and the Northwest Territories, Canada, in 2008. The 12-month program was developed from theory (social cognitive theory and social ecological models), formative research, and a community participatory process. It included an environmental component to increase healthy food availability in local stores and activities consisting of community-wide and point-of-purchase interactive educational taste tests and cooking demonstrations, media (e.g., radio ads, posters, shelf labels), and events held in multiple venues, including recreation centers and schools. The intervention was evaluated using pre- and postassessments with 246 adults from intervention and 133 from comparison communities (311 women, 68 men; mean age 42.4 years; 78.3% retention rate). Outcomes included psychosocial constructs (healthy eating knowledge, self-efficacy, and behavioral intentions), frequency of healthy and unhealthy food acquisition, healthiness of commonly used food preparation methods, and body mass index (kg/m(2)). After adjustment for demographic, socioeconomic status, and body mass index variables, respondents living in intervention communities showed significant improvements in food-related self-efficacy (β = 0.15, p = .003) and intentions (β = 0.16, p = .001) compared with comparison communities. More improvements from the intervention were seen in overweight, obese, and high socioeconomic status respondents. A community-based, multilevel intervention is an effective strategy to improve psychosocial factors for healthy nutritional behavior change to reduce chronic disease in indigenous Arctic populations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".