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Record W2947210932 · doi:10.1186/s12960-019-0370-8

A dental workforce strategy to make Australian public dental services more efficient

2019· article· en· W2947210932 on OpenAlexaff
Tan Minh Nguyen, Utsana Tonmukayakul, Hanny Calache

Bibliographic record

VenueHuman Resources for Health · 2019
Typearticle
Languageen
FieldDentistry
TopicDental Health and Care Utilization
Canadian institutionsRichmond Hospital
FundersAustralian Government
KeywordsWorkforceSalaryBusinessGovernment (linguistics)Public sectorOutreachDental auxiliaryMedicineEconomicsDentistryEconomic growth

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.264
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.038
GPT teacher head0.361
Teacher spread0.324 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations19
Published2019
Admission routes1
Has abstractyes

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