Dental Service Utilisation Among First Nations’ People in Southeast Queensland
Bibliographic record
Abstract
Introduction Accessing dental care is challenging for First Nations Australians due to various barriers, including competing priorities, waiting lists and lack of available culturally appropriate care. This study aims to describe public dental services utilisation and completion of care in First Nations Australians. Methods A retrospective audit of administrative data from public sector dental services was undertaken for a 12‐month period in Southeast Queensland, Australia. A patient’s treatment needs are determined and are referred to as a “course of care” (COC). Nonidentified persons are defined as individuals who did not identify as belonging to Aboriginal and/or Torres Strait Islander communities or people. Attendance and nonattendance to individual dental appointments were recorded in electronic health records and extracted for analysis. Poisson regression with generalised linear modelling was used to calculate annual rates, and 95% CI per 100 appointments were calculated for attended appointments, nonattendance and COC completion. Results The overall proportion of attendance to appointments was higher in nonidentified patients, with 74.1 (95% CI: 73.5, 74.7) per 100 appointments attended for nonidentified and 66.2 (95% CI: 64.4, 68.0) per 100 appointments for First Nations. The largest difference in attendance rates was 13.1% lower by First Nations patients in the adult service in general dental appointments, where nonidentified attendance rate was 73.6% (95% CI: 72.2, 75.1) and First Nations attendance rate was 60.5 (95% CI: 58.4, 62.7). The overall rate of completion of COC was 68.7 (95% CI: 66.5, 70.9) per 100 and 77.9 (95% CI: 77.4, 78.3) per 100 for First Nations and nonidentified, respectively. Conclusions These findings underscore the need for targeted strategies to address difference in dental care attendance and completion between First Nations and non‐First Nations patients. Attendance rates for specialist and emergency care were comparable between First Nations and nonidentified individuals in this study, and there was a marked decrease in attendance for general services among adults overall. Solutions for access to oral health care must include active participation and engagement of the First Nations community, working hand‐in‐hand with the health service, to codesign and implement culturally appropriate solutions.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".