Use of Traditional Chinese Medicine by older Chinese immigrants in Canada
Bibliographic record
Abstract
BACKGROUND: Research is needed about the usage of complementary and alternative medicines within culturally diverse groups because of a growing number of people who use these remedies. OBJECTIVE: To understand the prevalence and predictors of Traditional Chinese Medicine (TCM) use by older Chinese immigrants in Canada. METHODS: This is based on the data collected from a representative sample of 2167 elderly Chinese immigrants aged 55 years and above in seven Canadian cities. Logistic regression was used to estimate the probability of using TCM in combination with Western health services (WHS). Use of Chinese herbs, herbal formulas, and TCM practitioners (herbalists) was predicted, based upon the effects of predisposing, enabling and need factors. RESULTS: The response rate was 77%. Over two-thirds of the older Chinese immigrants reported using TCM in combination with WHS. About half (50.3%) of the older Chinese immigrants used Chinese herbs, 48.7% used Chinese herbal formulas, and 23.8% consulted a Chinese herbalist. Although separate analysis was conducted, similar predictors were identified. Country of origin, Chinese health beliefs, social support, city of residency, and health variables were the common predictors of using a form of TCM. CONCLUSION: The combined use of TCM and WHS is common among elderly Chinese immigrants. Culture-related variables are important in determining use of TCM. The predictors identified should help physicians to recognize who among the elderly Chinese immigrants are more likely to use TCM so that a more in-depth understanding toward their health practices and needs can be achieved.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".