University Students' Eating Behaviors: An Exploration of Influencers.
Why this work is in the frame
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Bibliographic record
Abstract
Problem There is evidence that university students have poor eating behaviors that can lead to short and long term negative health effects. Understanding the influences on eating behaviors will aid universities and health agencies in developing effective healthy eating promotion strategies. Purpose and Method To determine the impact of a range of influencers on healthy eating behaviors, a tested and ethics approved questionnaire was distributed to a random sample of students at two universities. Responses (n=188) were statistically analyzed and logistic regression was conducted. Results Mean daily food group servings were below recommendations for the vegetables/ffuits and grain products groups. The regression models for minimum vegetable/fruit group were statistically significant for healthy eating, media/social and the professional advice influencer scales. For the meal/altemates, the models were significant for budget constraints, professional advice and nutrition self-efficacy influencer scales. No significant relationships were found for the other two food groups. Conclusions There is a need to improve the eating behaviors of university students and different influences affect consumption of different food groups. A focus on particular influences can enable a targeting of healthy eating promotion and communication strategies on deficient food groups. Introduction University students are at a critical phase in their lives and making decisions about their health and, in particular, eating behaviors. However, there is evidence that these decisions need improvement. It has been reported that the diets of young adults, females in particular, lacked vegetables, fruits and milk, but were high in fat and sugars (Garriguet, 2007; Statistics Canada, 2013; Centre of Disease Control and Prevention [CDC], 2015). This has likely contributed to over 50% of Canadians reported to be overweight and over 20%, obese (Statistics Canada, 2014a), and similarly, an obesity rate of 35% for adults in the USA (CDC, 2015). Health risks associated with poor eating behaviors, overweight, and obesity, include diabetes, heart disease and cancer (Von Ah, Ebert, Ngamvitroj, Park & Duck-Hee, 2004; Boyle & LaRose, 2009; Gibney, Lanham-New, Cassidy & Vorster, 2009; World Cancer Research Fund, 2007) as well as short-term effects such as fatigue, stress, decreased ability to concentrate and poor body image (Hol-Denoma, Joiner, Vohs & Heatherton, 2008; Kandiah, Yake, Jones & Meyer, 2006; Gores, 2008). Therefore, in order to maximize the academic and social development potential for university students, healthy eating behaviors need to be established and/or reinforced. Understanding the complex relationships among individual and environmental influences, as described by the determinants of healthy eating (Raine, 2005; LaCaille, Sauner, K ram beer & Pedersen, 2011), can assist universities and health agencies to develop effective health promotion and support strategies. The purpose of this study was to determine the impact of selected influences on the self-reported food frequency intakes of a random sample of univesity students. The influences included perceptions about personal health and lifestyle (Boyle & LaRose, 2009; Kandiah et al, 2006; Vaex, Kristenson, & LaFlamme, 2004; Sun, 2008; Jackson, Berry & Kennedy, 2009; Paquette, 2005), healthy eating behavior (Taylor, Evers & McKenna, 2005; House, Su & Levy-Milne, 2006; Kolodinsky, Harvey-Berino, Berline, Johnson & Reynolds, 2007; Ha & Caine-Bish, 2009), the impact of budget constraints (Vaez, et al, 2004; House et al, 2006; Garcia, Sykes, Matthews, Martin & Leipert, 2010; Brown, Dresen & Eggett, 2005; Deshpande, M.D. Basil & D.Z. Basil, 2009), nutrition self-efficacy (Von Ah, 2005; Boyle & LaRose, 2009; Deshpande et al, 2009; Kim, Ahn & No, 2012; Lockwood & Wohl, 2012; Yilmaz, 2014) and various information sources, including family, friends, professionals, media and websites (House et al, 2006; Lockwood & Wohl, 2012; Ostry, Young & Hughes, 2008; Freisling, Haas & Elmadfa, 2009; Lee, 2010). …
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it