ncovering the Complexity of Food/Nutrition, Physical Activity and Mental Health among Arab Immigrants/Refugees in Ontario, Canada: The Can-Heal Study
Bibliographic record
Abstract
This doctoral dissertation explores the complex food/nutrition, leisure physical activity (LPA) and mental health (MH) needs in Arab immigrants/refugees (AIR) in Ontario, Canada. The main goal is to improve the MH and well-being of AIR. The CAN-HEAL (Canadian Arab Nutrition, Health Education and Active Living) project used a collaborative community-based participatory research and integrated knowledge translation approach, and triangulated data from three different methods (qualitative interviews, Photovoice, and a questionnaire survey) to enhance study rigour. A primary finding of this research is that food/nutrition, LPA, and MH needs in AIR are multi-layered and vary considerably according to intersectional experiences, cross-cultural pressures, living conditions and racism. The research found an alarming prevalence of poor mental well-being (55%), food insecurity (65%) and low LPA levels (87%) in AIR participants (n=60). Among first-generation immigrant participants, 87% reported negative changes in MH since immigration. These negative changes are not straightforward; they are complex and dynamic, and mainly related to structural barriers, poor living conditions, and system failures to accommodate the distinct cultural needs of the AIR community. Intersections among different socio-demographic factors (e.g., gender, length of residency, income, parenthood, religion, immigration status), amplified the negative changes in MH, and played a considerable role in how nutrition, food security and LPA impacted AIR’s MH, exacerbating inequities within the AIR community. This research shows that the relationships among food/nutrition, LPA and MH among AIR are multi-faceted, and that there are various psycho-socio-cultural pathways and processes through which diet quality, cultural foods and LPA can contribute to shaping AIR’s MH. As part of this research, an upstream-downstream-based socio-political and community-level action plan was co-developed to thoroughly address the complex needs among AIR and to work towards health equity for this marginalized population. Collaboration between health and non-health sectors is required to effectively implement this action plan.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".