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
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".