Lifestyle and Health Habits Among Canadian University Community
Bibliographic record
Abstract
PURPOSE: It is estimated that by 2019, 55.4% of the Canadian adult population will be categorized as overweight (34.2%) or obese (21.2%) (Twells et al., 2014). Increase of obesity prevalence, calls for continuous search of effective obesity-prevention and health promotion strategies. Following the work of Perusse-Lachance and colleagues (2010), the present study aimed to further examine the prevalence of obesity and lifestyle habits through environmental factors in a different Canadian university community. METHODS: A web based-survey assessing lifestyle habits such as physical activity and nutrition was sent by email to all university’s students (n=14 500) and employees (n=2000). Students or employees had to be registered as part or full time during the 2016 fall semester to participate in the study. Response rate was 13.3% for students (n=1 989) and 24.3% for employees (n=485). All data were analyzed using SPSS. PRELIMINARY RESULTS: Results showed that 18.7% of students and 28.5% of staff members were overweight (BMI = 25.0-29.9), 10% of students and 10.7% of staff members were obese (BMI ≥ 30.0) while 40% of students and 37% of the employees were currently trying to lose weight. Results also revealed that 50% of staff members and 57% of students were considered sedentary (<150 minutes of physical activity/week). Regarding fruits and vegetables intake, only 14% of students consumed four or more servings of fruits per day compared to 15% for staff members. In addition, only 15% of students consumed four or more vegetables servings per day as opposed to 26% for staff members. Finally, 91% of employees, compared to 81% of the students, were having breakfast every day. CONCLUSION: Healthy environments are crucial in the adoption of healthy behaviors (Booth et al., 2001). In this regard, the herein results suggest that overweight and obesity can be an important concern in a well-educated sample and that this issue is associated with various health-related behaviors. These conclusions highlight the need to develop specific health promotion strategies in this Canadian University community.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".