Evaluation of dietary patterns of nutrition undergraduates
Notice bibliographique
Résumé
Objective: In an era in which chronic non-communicable diseases predominate, promoting healthy lifestyles and lifestyle-modifying therapies dominate the language of health professionals. However, epidemiological data on the prevalence of obesity and overweight remain alarming for young people. This is also true for shocking eating habits were fast-food dominates the choices of the younger [1 Durán Agüero S, Fernández Godoy E, et al. Food patterns associated with a healthy body weight in chilean students of nutrition and dietetics. Nutr Hos. 2015;32(4):1780–1785.[PubMed], [Web of Science ®] , [Google Scholar]]. Knowing that it will be the current young students of health courses who will work in this hostile environment, where obesity and associated comorbidities prevail [2 Mahn HM, Lordly DA. Review of Eating Disorders and Disordered Eating amongst Nutrition Students and Dietetic Professionals. Can J Diet Pract Res. 2015;76(1):38–43. doi:10.3148/cjdpr-2014-031[Crossref], [PubMed], [Web of Science ®] , [Google Scholar],3 Strawson C, Bell RC, Farmer A, et al. Changing Dietary Habits of Alberta Nutrition Students Enrolled in a Travel Study Program in Italy. Can J Diet Pract Res. 2015;76(2):93–96.[Crossref], [PubMed], [Web of Science ®] , [Google Scholar]], our objective was to evaluate the nutritional intake of nutrition students as literature seems to point to some level of disordered eating amongst Nutrition Students.Material and methods: This was a cross-sectional study enrolling students from the Nutrition Undergraduation of a Private University, developed in the context of two disciplines: Evaluation of Food intake, and Introduction to Research Methods. Students were invited to participated by personal contact. Food intake was assessed using a validated Food Frequency Questionnaire (FFQ), developed to estimate dietary intake across Europe. This FFQ comprises seven options to report food consumption frequency: 2+ per day, once per day, 5–6 per week, 2–4 per week, once per week, 1–3 times per week, and rarely or never. Analysis of food groups in terms of frequency of intake was performed. Study performed according to Helsinki Declaration.Results: From a total of 86 students, 62 agreed to participated in the study (age range 20-43 years, 53 females). It was observed that in terms of frequency of the main food groups, this sample is accomplishing some of the objectives of the Portuguese Food Wheel as evidenced in Figure 1. Surprisingly, we found higher intakes of whole grains than those observed for the general population, as well as lower intake report of fast food, alcoholic drinks, chocolate and soft drinks. However, frequency intakes reported for fish and legumes are worrisome. Also, there is a considerable percentage of students who reported to rarely eat carbohydrate-rich and protein-rich foods. 27,4% of students are using supplements.Evaluation of dietary patterns of nutrition undergraduatesAll authorsRenata Ramalho, Adriana Condeça, Ana Anjos, Catarina Lôpo, Débora Soares, Gonçalo Neto, Inês Sousa, Lara Santos, Marisa Luz, Marta Lopes, Marta Caldeira, Patrícia Gonçalves, Sara Guilherme, Sofia Castanheiro, Mariana Medeiros, Luís Nogueira & Paula Pereirahttps://doi.org/10.1080/07853890.2018.1561996Published online:28 May 2019Figure 1. Frequency of intake of principal food groups.Display full sizeFigure 1. Frequency of intake of principal food groups.Conclusion: We observed that nutrition undergraduates reported high intakes of some healthy foods, particularly whole grains, vegetables and fruits and low intake of fast-foods and softs drinks. These results may be explained by intrinsic knowledge on food and nutrition provided by the Undergraduation, greater awareness of the impact of nutrition on health, bias of the methodology by responding to what is desirable, or a combination of all these possibilities. However, intakes of legumes and fish are lower than desired. These results raise a pertinent question: are nutrition undergraduates eating healthier than the general population or do they respond to some items according to what they believe to be expected of them?
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 | 0,001 | 0,005 |
| 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 | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
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 ».