Social determinants in dental health of young Chinese immigrant children in British Columbia, Canada
Bibliographic record
Abstract
Background Immigrant children are disproportionately affected by dental caries worldwide. Limited research has examined the factors influencing the dental care of children from Chinese immigrant families. This qualitative study examines the multi-level factors associated with dental health and care practices of young Chinese immigrant children (ages 0-6) in British Columbia, Canada. Methods Semi-structured individual interviews were conducted with parents (n = 15), dental professionals (n = 4), and social service providers (n = 3) recruited in Vancouver and Richmond, British Columbia. Transcribed interviews were analyzed using thematic analysis. Results Major barriers include: (1). Economic. High cost and no dental insurance discourage access to professional dental services (2). Language. Inadequate language proficiency and lack of translated resources limit access to accurate dental care information (3). Culture. Lack of awareness of importance of dental care in early years prevent early dental care for children. Key facilitators are: (1). Community. Dental promotional programs support families to initiate and maintain effort in dental home care for children (2). Social network. Friends and family members support by being directly involved in care or providing advice (3). Dental professionals. Dental visits or community dental programs are opportunities for soliciting feedback on the adequacy of dental home care practices. Conclusions (1). Insurance. Government provides dental insurance for purchase at subsidized rates by income levels. (2). Information. Health professionals develop consistently translated dental care information with culturally-appropriate recommendations targeting first-time parents and broader immigrant community (3). Community engagement. Community partners use existing community network and social media to engage immigrant parents in early dental care for children. Key messages: Dental public health professionals may improve care provision with enhanced understandings of the common challenges faced and supports needed by the Chinese immigrant families with young children. Study findings contribute to inform improvement in current dental public health programs to better engage Chinese immigrant families in caring for children’s dental health.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".