University Students' Eating Behaviors: An Exploration of Influencers.
Notice bibliographique
Résumé
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). …
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».