First Nations Peoples’ Eating and Physical Activity Behaviors in Urban Areas: A Mixed-Methods Approach
Bibliographic record
Abstract
The dietary transition from traditional to commercial foods and a decrease in physical activity (PA) have impacted the health of the First Nations people of Quebec (Canada), resulting in many suffering from multiple chronic diseases. This study had two objectives: (1) to examine eating and PA behaviors among First Nations peoples in urban areas and (2) to explore the associated health representations. To achieve these objectives, a mixed-methods approach, including a questionnaire (n = 32) and a semi-structured interview (n = 14), was used to explore the participants’ lifestyle profiles and health experiences. The questionnaire focused on the eating and PA behaviors of First Nations people and their underlying motivations. At the same time, the interviews investigated their health views on diet and PA behaviors based on the conceptual framework of health and its determinants. According to the participants, health is the autonomy to live without pain by maintaining a balance between physical and psychological aspects, eating healthy and exercising. Family and work influence participants’ PA and eating behaviors. Exploring First Nations people’s beliefs and perceptions and the motivations underlying their health behaviors could help encourage the maintenance of a healthy lifestyle despite multiple chronic health conditions.
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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 | 0.014 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".