Influence of an innovative game on nutrition education for nursing students
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Notice bibliographique
Résumé
Background and objective: The growing global burden of individuals with malnutrition shows no signs of decreasing. Schools are an important arena for health promotion. This study is to develop and to test the innovative game with Good Food cards designed to enable students to acquire knowledge in healthy eating and other influence on their eating behaviors as well as nursing role in dietary education.Methods: Design: A mixed method design was used to evaluate the effectiveness of the game. We conducted pretest-posttest quasi-equivalent groups with cluster sampling design and recruited 109 students at two sites of health center. The game intervention was implemented in two to four 20- to 30-minute sessions for 55 junior nursing students. Method: Univariate analysis of covariance (ANCOVA) was performed to compare knowledge change between the two groups. Additionally, a focus group was performed to collect data from students. Audio-recorded, transcribed verbatim and thematically analyzed were conducted by researchers. Correlation analysis was used to was used to explore factors related to the concept and behavior intention.Results: The knowledge on healthy diet was significantly higher in the experimental group compared to the control group. Effective learning experiences and intention to change eating behaviors were also perceived by participants.Conclusions: The Good Food cards and game mechanism matched the learning objectives of the dietary education. Gaming is a valid learning strategy not just a fun activity. During nursing practicum period, students who are familiar with healthy diet concept perceived the help to facilitate therapeutic relationship building with clients and more willingness to change eating habits. More research is needed to test other children, students, or adult population with this card. Also, factors related to concept and behavior change from playing with this card can be investigated to strengthen students’ competence and focusing more strongly on how best to influence students’ healthy dietary behavior.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 découle