Guest Editorial on the Festschrift “Challenges in population oral health for the 21st Century”
Bibliographic record
Abstract
This Festschrift provides important and valuable guidance on population oral health. It is unfortunate that it coincides with the retirement of one of population oral health's pioneers, Professor John (AJ) Spencer, who has made significant contributions to the subject over the last 35 years. Oral diseases and disorders remain prevalent and have detrimental effects on individuals, and society-at-large. Many of our attempts to improve population oral health through existing oral health services have met with limited success, owing to the focus on 'care' rather than 'cure'. It has long been recognised that oral diseases are largely behavioural in origin; but this approach at the individual level has not been entirely successful. There is a need to consider 'mid- and up-stream' approaches to changing oral health and health care delivery systems. However social inequalities are a major determinant of oral health, and their influence in oral health are omnipresent, which emphasises the important role of population oral health in future health care strategies. The Festschrift provides theoretical and empirical evidence of the need for further epidemiological investigations (particularly life course studies), innovative approaches to oral health surveillance and oral health outcome evaluation, plus a realisation that multidisciplinary approaches are the way forward. In an information and globalization era, we must put the 'population' into oral health and this is our challenge for the 21st Century.
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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 | 0.010 | 0.039 |
| Meta-epidemiology (narrow) | 0.006 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.017 | 0.029 |
| Insufficient payload (model declined to judge) | 0.012 | 0.011 |
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".