Planning the oral health workforce: Time for innovation
Bibliographic record
Abstract
The levels and types of oral health problems occurring in populations change over time, while advances in technology change the way oral health problems are addressed and the ways care is delivered. These rapid changes have major implications for the size and mix of the oral health workforce, yet the methods used to plan the oral health workforce have remained rigid and isolated from planning of oral healthcare services and healthcare expenditures. In this paper, we argue that the innovation culture that has driven major developments in content and delivery of oral health care must also be applied to planning the oral health workforce if we are to develop 'fit for purpose' healthcare systems that meet the needs of populations in the 21st century. An innovative framework for workforce planning is presented focussed on responding to changes in population needs, service developments for meeting those needs and optimal models of care delivery.
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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 | 0.032 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".