Healthy Active Living Programs for New Immigrant Families in High-income Countries: A Scoping Review
Bibliographic record
Abstract
Literature Review: Health screening criteria in most categories of immigration typically ensure that new immigrants to Canada are generally healthier (in terms of chronic conditions) than their Canadian-born counterparts of a similar age, income, and education level (1)(2), a phenomenon known as the "healthy immigrant effect," which has long been observed in Canada (1). Typically, this advantage declines as years in Canada increase because immigrants adopt the diet and lifestyle patterns of their new country of landing (2)(3). Changes in the physical (e.g., weather) and cultural environment (e.g., language, social norms), as well as food availability (i.e., traditional/familiar foods), physical activities, and economic constraints, all contribute to the adoption of new health routines (4). Sometimes, these new health routines disrupt or replace traditional health practices that, if continued, may influence risk factors for developing chronic diseases in Canada. Changes in Physical Activity (PA), including structured exercises, leisure time physical activities, physical transportation activities, and employment physical activity patterns among new immigrants to a high-income country, especially first-generation non-English speaker adolescents, are evident in different research (5)(6)(7). Multiple studies suggested that different factors, such as neighborhood, environmental factors, and the influence of schools, might play a role in underlying disparities (4)(6)(8). Purpose: Lack of PA contributes to Sedentary Behaviour (SB) among immigrant adolescents and can continue to adulthood, lead to overweight and obesity, and deteriorate the health outcome to type 2 diabetes and cardiovascular diseases (9)(10)(11). The positive association between recent immigrants and less PA indicates possible health disparities, suggesting further research needs. Cultural, social, and environmental contexts can be considered when designing any "healthy active living intervention" program to improve the health outcomes for the new immigrant family. This proposed scoping review aims to assess the available literature on Canada's new immigrants' "healthy active living" issues and identify potential priorities for further research needs.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".