Dissemination and Implementation Science for Oral Health: Why not consider de-implementation of low-value care to target oral health equity?
Bibliographic record
Abstract
Dissemination and implementation sciences provide oral health professionals with an opportunity to understand which determinants promote the adoption of evidence-based innovations and interventions. Within this dynamic field, de-implementation provides the other side of the coin, that is, finding the ways to halt or modify practices that may be harmful to patients, do not hold sufficient scientific backing, or are simply not cost-efficient; conjointly known as low-value care. Scrutinizing low-value care procedures in oral health is essential, as identifying such practices creates opportunities to replace, update, or enhance them with approaches that offer greater benefits to patients, practitioners, and healthcare systems. Effective de-implementation begins with a clear understanding of which low-value practices persist. Only then can we conceptualize strategies to overcome barriers and promote more efficient, evidence-based care within the field. Furthermore, de-implementation can serve as a way to target oral health equity, as shifting away from low value care practices can lead to the better use of human and economic resources in those places where it is needed most. This commentary serves as a conceptualization of de-implementation within oral health, as well as, an invitation for the broader community to reflect on the importance of adequately mobilizing towards the delivery of more equitable care using the vigorous elements that dissemination and implementation science offer.
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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 | 0.370 | 0.633 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.007 | 0.044 |
| Scholarly communication | 0.023 | 0.031 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.044 | 0.051 |
| Insufficient payload (model declined to judge) | 0.004 | 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".