Health, Psycho-social and Cultural Determinants of Medication Use by Chinese-Canadian Older Persons
Bibliographic record
Abstract
ABSTRACT Variations in health and medication use exist across cultures. Medication use among Chinese-Canadian older persons is complicated by many factors including combined use of Western and traditional Chinese medicines (TCM). There is little research on health, psycho-social and cultural determinants of medication use in the Chinese. A cross-sectional census study of community-based Chinese-Canadian older persons in the Kitchener/Waterloo area was conducted using the Minimum Data Set for Home Care and a supplementary questionnaire for cultural issues. The response rate was 89.1 per cent with 106 participants using face-to-face assessments. Socio-demographic and cultural variables were summarized. The multivariate logistic model for TCM use included pain symptoms and being hospitalized, and a curvilinear association between TCM use and health beliefs. For combined medicine use, living with a child, pain symptoms, hospitalization, and social isolation problems were the main effects. Living with a child, physical health problems and number of diseases were associated with Western medicine use. Health, psycho-social and cultural factors were significant determinants for medication use. Education programs for both Chinese-Canadian older persons and health care providers are necessary to understand the appropriate use of Western and TCM treatments.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".