Exploring child dental service use among migrant families in metropolitan Melbourne, Australia
Bibliographic record
Abstract
BACKGROUND: This study describes and explores factors related to dental service use among migrant children. METHODS: A cross-sectional analysis of baseline data from Teeth Tales, an exploratory trial implementing a community based child oral health promotion intervention. The sample size and target population was 600 families with 1-4 year old children from Iraqi, Lebanese and Pakistani backgrounds residing in metropolitan Melbourne. Participants were recruited into the study using purposive and snowball sampling techniques. RESULTS: Most (88%; 550/625) children had never visited the dentist (mean (SD) age 3.06 years (1.11)). In the fully adjusted model the variable most significantly associated with child dental visiting was parent reported 'no reason for child to visit the dentist' (OR = 0.07, p < 0.001). Of those children whose parents reported their child had no reason to visit the dentist, 22% (37/165) experienced dental caries with 8% (13/165) at the level of cavitation. CONCLUSIONS: Dental service use by migrant preschool children was very low. The relationship between perceived dental need and dental service use is currently not aligned. One in 10 children of select migrant background had visited a dentist, which is in the context of 1 in 3 with dental caries. To improve utilization, health services should consider organizational cultural competence, outreach and increased engagement with the migrant community.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".