Body weight misperception among Chinese international students in Canada \nduring the COVID-19 pandemic
Notice bibliographique
Résumé
The phenomenon of BWM (body weight misperceptions) has been linked to a range of \nhealth risks. Unfortunately, the COVID-19 outbreak may have exacerbated this issue, leading to \ndetrimental weight fluctuations and an increased susceptibility to BWM. This study investigates \nBWM and its association with sociodemographic and lifestyle behaviors factors and self-perceived \nmental, physical, and overall health among Chinese international students in Canada during the \nsecond wave of the COVID-19 pandemic in early 2021. Data were collected from 296 eligible \nstudy participants through targeted sampling. Bivariate descriptive analyses and multivariate \nbinary logistic regression analyses (BLR) were used. \nThe study found that (29.1%) had overweight and (7.9%) had underweight misperceptions \namong Chinese international students in Canada. The study found that females had a higher \nlikelihood of reporting overweight misperceptions (OR=3.18, CI=1.39-7.24), while financial \ndissatisfaction and lifestyle behaviors such as watching television were associated with a higher \nrisk of overweight misperception (OR = 2.84, 95% CI = 1.39-5.82 and OR = 1.92, 95% CI = 1.09– \n3.34, respectively). On the other hand, exercise was associated with a lower risk of overweight \nmisperception (OR = 0.56, 95% CI = 0.32-0.98). This study also found that overweight \nmisperception have a lower likelihood of having poor overall health (OR = 0.61, 95% CI = 0.39- \n0.95), but no significant association with mental health (OR = 1.35, 95% CI = 0.86-2.11), or \nphysical health (OR = 1.15, 95% CI = 0.75-1.77). However, underweight misperception was \nassociated with a higher likelihood of poor overall health (OR = 1.54, 95% CI = 1.03-2.38), but \nno significant association was found with self-reported physical health (OR = 1.17, 95% CI = 0.78- \n1.76) and mental health (OR = 1.03, 95% CI = 0.67-1.56). \nIn conclusion, the study highlights that overweight misperceptions are prevalent among \nChinese international students in Canada, particularly among female and those who are financially \ndissatisfied and watch television. Exercise was found to lower the risk of overweight \nmisperception. Underweight misperception was associated with poor overall health. The study \nhighlights the need for targeted interventions to promote healthy lifestyles and well-being, and \nfurther research is required to identify additional factors and develop effective interventions.
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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 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».