A dental workforce strategy to make Australian public dental services more efficient
Bibliographic record
Abstract
BACKGROUND: Dental services can be provided by the oral health therapy (OHT) workforce and dentists. This study aims to quantify the potential cost-savings of increased utilisation of the OHT workforce in providing dental services for children under the Child Dental Benefits Schedule (CDBS). The CDBS is an Australian federal government initiative to increase dental care access for children aged 2-17 years. METHODS: Dental services billed under the CDBS for the 2013-2014 financial year were used. Two OHT-to-dentist workforce mix ratios were tested: Model A National Workforce (1:4) and Model B Victorian Workforce (2:3). The 30% average salary difference between the two professions in the public sector was used to adjust the CDBS fee schedule for each type of service. The current 29% utilisation rate of the CDBS and the government target of 80% were modelled. RESULTS: The estimated cost-savings under the current CDBS utilisation rate was AUD 26.5M and AUD 61.7M, for Models A and B, respectively. For the government target CDBS utilisation rate, AUD 73.2M for Model A and AUD 170.2M for Model B could be saved. CONCLUSION: An increased utilisation of the OHT workforce to provide dental services under the CDBS would save costs on public dental service funding. The potential cost-savings can be reinvested in other dental initiatives such as outreach school-based dental check programmes or resource allocation to eliminate adult dental waiting lists in the public sector.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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".