Pattern and determinants of Traditional Chinese Medicine use for upper respiratory tract infection among adults attending primary care clinics
Bibliographic record
Abstract
Objective: (1) To explore the pattern of Traditional Chinese Medicine (TCM) use for Upper Respiratory Tract Infection (URTI); and, (2) to identify the determinants associated with such health-seeking behaviours. Design: Cross-sectional survey. Subjects: Adults (aged 18-59) attending the three designated primary care clinics from February 2006 to April 2006. A total of 399 questionnaires were distributed; 381 (95.5%) valid for analysis. Main outcome measures: Demographic data; details of reported URTI episodes; TCM used for the reported URTI episodes. Independent variables were tested by binary logistic regression. Multiple logistic regression analysis was conducted for variables with p<0.05 to determine possible independent predictors of TCM use in treating URTI. Results: 38.1% of all respondents tried at least 1 type of TCM during URTI episode. A quarter used western medicine and TCM either simultaneously (11.3%) or in the recovery stage of their illness (11.8%). Age and satisfaction scores for western medicine and TCM were shown to be independent predictors associated with TCM use in treating URTI. Conclusion: Use of TCM to treat URTI among adults attending private clinic is common especially among older patients. All general practitioners should be aware as a significant portion of TCM use happens while people are taking prescribed medications.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".