Socioeconomic Determinants of Physical Activity among Adult Arab Immigrants in Edmonton, Alberta
Bibliographic record
Abstract
Little is known about leisure-time physical activity (LTPA) habits of Arab immigrants in Canada. Leisure-time physical activity has been linked to decreased risks for cancer, cardiovascular disease, and all causes mortality and increased life expectancy. Socioeconomic status has been recognized as a significant factor affecting health and wellbeing due to its impact on individuals’ attitudes, experiences, and exposure to several risk factors. The purpose of this cross-sectional descriptive study was to explore the levels of participation in LTPA among adult Arab immigrants in central Alberta, Canada, to examine the socioeconomic determinants of LTPA, and to investigate which individual, social, and environmental factors contribute to LTPA participation. Electronic surveys were used to collect data from a sample of 376 adults. The socioecological model and systems theory were used as the theoretical foundations to guide this research. Descriptive and multiple regression analyses were performed using SPSS. Around 40% of participants were physically active. As participants attained higher degrees, earned more money, and had occupations requiring less physical effort, their levels of LTPA increased. The social conditions in which the participants live also affected their levels of LTPA. Being more familiar with the health benefits and having fewer barriers to exercise predicted an increase in LTPA, whereas higher self-efficacy seemed to predict a decrease in LTPA. Family and friends’ support for exercise increased the levels of LTPA of participants. And finally, more environmental support for exercise predicted a decrease in LTPA levels among participants. Findings from this research have the potential to design and implement targeted LTPA recommendations and interventions for Arab immigrants.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".