Exploring 24-Hour Movement Behaviours, Resettlement Dynamics, and Immigrant Well-being
Bibliographic record
Abstract
Understanding movement behaviours among immigrants in relation to their resettlement can inform public health efforts to promote immigrant well-being. There is a paucity of information about the relationship between movement behaviours and immigrant health, and how these movement behaviours relate to time since immigration. Further, little is known about the challenges and opportunities that characterize immigrants’ resettlement journey as it relates to movement and well-being, nor what constitutes the most effective ways to promote movement behaviours among immigrants. Thus, the overarching objective of this dissertation is to examine the prevalence of movement behaviours, resettlement dynamics, and movement intervention effectiveness among immigrants. Manuscripts one and two report on the prevalence of immigrants meeting the 24-Hour Movement Guideline recommendations, the association of movement behaviours with diverse well-being indicators, and how these differ by time since immigration. Using pooled data from the Canadian Community Health Survey (CCHS), we found that about than one in five adult immigrants met all three 24-hour Movement Guideline recommendations. Similar to non-immigrants, over half of the immigrants met MVPA recommendations. Recent immigrants were more likely to meet screen time, sleep duration, and all three recommendations than non-immigrants. Recent immigrants were also more likely to meet sleep recommendations and meet three recommendations than established immigrants. Manuscript three was guided by a community-engaged research approach to explore resettlement experiences, physical activity behaviour, and belonging through interviews with newcomer women in Canada. A reflexive thematic analysis highlighted the challenges of resettlement including cultural differences, isolation, and lack of social support. Physical activity participation was seen as a means to achieving well-being and community belonging by providing an opportunity to build relationships with Canadians and other immigrants. Manuscript four systematically reviewed the effectiveness and types of interventions targeting movement behaviours among immigrants in immigrant-adopting countries. We conducted a narrative synthesis of relevant evidence and found that most interventions were multicomponent and culturally tailored. These interventions were effective in improving at least one of the targeted movement behaviours. Findings from this dissertation have important implications for public health policies, interventions and research related to immigrant resettlement, health, and well-being.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".