Discussions on oral health care among elderly Chinese immigrants in Melbourne and Vancouver
Bibliographic record
Abstract
BACKGROUND: This study explored how elderly Chinese immigrants value and relate to how acculturation influences oral health and subsequent service use. METHODS: Elders who had immigrated to Melbourne and Vancouver within the previous 15 years were recruited from local community centres and assigned to focus groups of 5-7 participants in Vancouver (4 groups) or Melbourne (5 groups). RESULTS: Following an iterative process of thematic analysis, the discussions revealed that immigrants care about the comfort and appearance of their teeth, and they value Western dentistry as a supplement to traditional remedies, but they have difficulty getting culturally sensitive information about oral health care. Accessing dentistry, they explained, is distressing because of language problems and financial costs that impose on their children. Consequently, many immigrants obtain dental treatment in China when they return for occasional visits. They felt that separation of dentistry from national health care programmes in Canada and Australia disregards natural links between oral health and general health. CONCLUSIONS: The similarity of concerns in both cities suggests that dissemination of information and availability of services are the important themes influencing oral health, and that, beliefs developed over a lifetime play an important role in interpreting oral health in the host country.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".