Attitudes of Chinese immigrants in Canada towards the use of Traditional Chinese Medicine for prevention and management of COVID-19: a cross-sectional survey during the early stages of the pandemic
Notice bibliographique
Résumé
OBJECTION: The objective of this study was to assess attitudes towards the use of Traditional Chinese Medicine (TCM) for COVID-19 among Chinese immigrants in Canada during the early stage of the COVID-19 pandemic. METHODS: A cross-sectional study was conducted in April 2020 in Canada. Individuals aged 16 or older who were of Chinese origin and living in Canada at the time of the survey were invited to participate in an online survey. Descriptive and univariate statistics were performed to describe participant attitudes towards various preventive and treatment measures for COVID-19. Multiple logistic regression was used to identify independent associations with sociodemographic factors and attitudes. RESULTS: A total of 754 eligible respondents were included in the analysis. 65.8% of the participants were female, 77.2% had a university degree or higher and 28.6% were 55 years of age or older. Overall, 48.8% of the study participants believed that TCM was effective in preventing COVID-19% and 46.2% would use TCM if they had COVID-19-related symptoms. However, the corresponding numbers for western medicine were 20.8% and 39.9%, which were statistically lower (p<0.01). Older participants (55+vs <35, OR=3.55 (95% CI 2.05 to 6.14); 35-54 vs <35, OR=1.98 (95% CI 1.27 to 3.08)) and those who were dissatisfied with their income (OR=2.47(95% CI 1.56 to 3.92)) were more likely to believe TCM was effective against COVID-19. Similarly, older participants (55+vs <35, OR=3.13 (95% CI 1.79 to 5.46); 35-54 vs <35, OR=2.25 (95% CI 1.35 to 3.74)), females (OR=1.60 (95% CI 1.15 to 2.23)), and those born in mainland China (OR=10.49 (95% CI 2.32 to 47.39)) were more likely to use TCM if they had symptoms of COVID-19. CONCLUSION: Despite the lack of scientific evidence to support its use, TCM was widely believed by Chinese immigrants in Canada to be an effective means of preventing COVID-19 and many also stated they would use it if they were experiencing symptoms of COVID-19.
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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 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| 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) | 0,002 | 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 ».