Change in Dental Visits Among Eligible Children Under the Impact of the Child Dental Benefits Schedule in Australia
Bibliographic record
Abstract
OBJECTIVES: In Australia, although there have been some improvements, child oral health continues to be a major public health issue. The Australian Government introduced the means-tested Child Dental Benefits Schedule (CDBS) in 2014 to support access to dental services for children and adolescents aged 0-17 years from low-income families. There is a lack of evidence documenting whether the CDBS improved the dental attendance rate. This study aimed to evaluate the impact of the CDBS on dental visits among eligible children and adolescents in Australia. METHODS: The study analysed the data set from the birth cohort (B cohort) in the Longitudinal Study of Australian Children (LSAC). This is a nationally representative cohort survey collected biennially since 2004. The information on dental visits in the last 12 months was reported by the parents. A difference-in-differences analysis was used to examine 22,985 observations in the period 2008-2018. A propensity score matching (PSM) method was employed as a robustness check for the main findings. RESULTS: The proportion of children and adolescents eligible for CDBS in the six biennial surveys from 2008 to 2018 was 62.0%, 54.4%, 47%, 41.2%, 35.5%, and 28.9%, while the proportion of eligible individuals visiting dentists was 38.0%, 45.6%, 53.0%, 58.8%, 64.5%, and 71.1%, respectively. The analyses showed that the CDBS policy had a statistically significant and positive impact on dental visits among eligible children and adolescents. There was a 6.1-6.4 percentage point increase (p-value < 0.001) in dental visits across different specifications after the introduction of the CDBS policy. CONCLUSION: The removal of financial barriers was beneficial to improve dental visits; however, the target group still faces the other remaining barriers, especially those related to inequalities in the social determinants of health, impeding the uptake of free dental services.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".