The Relationship between Lifestyle and Campus Eating Behaviours in Male and Female University Students.
Notice bibliographique
Résumé
Background. Poor nutritional practices and heightened levels of stress, two common attributes of university life, are strongly linked with weight gain and decreased health. Little research has examined the relationships between university students' lifestyle factors and campus eating behaviours; therefore, this study aimed to examine relationships between lifestyle and campus eating behaviour. Methods. Both lifestyle and eating behaviour questionnaires were developed and administered to male and female undergraduate students at a Canadian university (n=132). Participants were asked to complete the questionnaire during allotted class time or return the completed questionnaire at the following lecture. Data was analysed with a combination of correlation and difference testing. Results. Students whose living arrangements had not changed since high school consumed less alcohol than individuals who moved away from their previous dwellings. Fast food consumption was also significantly related to lower physical activity levels and higher expenditures for food on campus. Males also consumed more alcohol than females and spent more money for food on campus. Conclusion. Relationships do exist between lifestyle and campus eating behaviour. These results may be used as a foundation for future research on the effect of lifestyle on eating behaviours and nutritional status in university age students. Background Consideration for healthy eating, weight control and general wellness is of growing importance within Western society (Tremblay, Katzmarzyk, & Willms, 2002; Taylor, Evers, & McKenna, 2005). Despite this focus on wellness North Americans are on average becoming heavier, sleeping less, and experiencing more stress (Tremblay et al., 2002). Weight gain has been specifically linked to undergraduate university students who experience stress due to the workload of attending university (Serlachius, Hamer, & Wardle, 2007). In addition to the stresses of university life, the diet of the average university student is inadequate and reflects poor eating behaviours due to the price of healthy foods and exacerbated by easy access to fast food. Furthermore, these inadequacies tend to be gender dependent as females tend to choose healthier foods (Driskell, Meckna, & Scales, 2006). It is understood that university students also exercise less than the recommended guidelines and do not meet healthy lifestyle guidelines (Brevard & Ricketts, 1996; Driskell, Kim, & Goebel, 2005). General population research has shown that lifestyle and gender may have potentially significant influences on eating patterns and behaviours (Driskell et al., 2006; Spriegel et al., 2004; Schussler et al., 2006), however the eating and exercise behaviours of Canadian university students are not well described. Some research has described the lifestyle of students including how much time is spent on campus, involvement in extra-curricular activities, living arrangements, time spent working and volunteering, and time spent studying (Joyce, Hanson, Ebro, Ward, & Fair, 1996; Papadaki, Hondros, Scott, & Kapsokefalou, 2007). However, this research either focuses on the access to food and eating patterns or describes the lifestyle of students (Buscher, Martin, & Crocker, 2001; Driskell et al., 2005; Driskell et al., 2006). Thus the purpose of this investigation was to describe the on-campus eating behaviours of male and female undergraduate university students and to further determine the influence that select lifestyle factors may have on campus eating behaviours. The significance of this study is that it provides more information into the relationship of lifestyle and specific eating behaviours in university students. This information is of particular importance to the undergraduate student population and to university administrators because it may be a first step in determining which lifestyle factors most impact the eating behaviour and nutritional status of university students. …
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 | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».