Effectiveness of oral health promotion interventions: an Evidence Check rapid review brokered by the Sax Institute and commissioned by Dental Health Services Victoria for the Victorian Department of Health
Bibliographic record
Abstract
More than 63,000 Australians are hospitalised every year for preventable dental conditions, constituting the third most common reason for acute preventable hospital admissions. Yet oral diseases are largely avoidable with appropriate preventive measures. This Evidence Check aimed to find the most effective and relevant oral health promotion interventions for use in Australia. It covered systematic reviews of oral health promotion interventions from Australia, NZ, the UK, the US and Canada, finding 46 reviews which included a total of 1,026 individual studies. Twenty-five of these reviews only included randomised controlled trials and so they were the highest possible level of evidence. For the studies covering the broadest population groups, effective interventions included education, use of alternative sweeteners, use of fluoride toothpaste, smoking cessation, and referral to various dental practitioners. However, there were a limited number of studies conducted in Australia, and none focused on diverse populations such as people with disabilities or those on low incomes. This limits the generalisability of the findings to Australia and indicates significant gaps in the evidence base.
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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 | 0.065 | 0.133 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.009 |
| Bibliometrics | 0.021 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
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".