Behaviour support in dentistry: A Delphi study to agree terminology in behaviour management
Bibliographic record
Abstract
OBJECTIVES: Dental behaviour support (DBS) describes all specific techniques practiced to support patients in their experience of professional oral healthcare. DBS is roughly synonymous with behaviour management, which is an outdated concept. There is no agreed terminology to specify the techniques used to support patients who receive dental care. This lack of specificity may lead to imprecision in describing, understanding, teaching, evaluating and implementing behaviour support techniques in dentistry. Therefore, this e-Delphi study aimed to develop a list of agreed labels and descriptions of DBS techniques used in dentistry and sort them according to underlying principles of behaviour. METHODS: Following a registered protocol, a modified e-Delphi study was applied over two rounds with a final consensus meeting. The threshold of consensus was set a priori at 75%. Agreed techniques were then categorized by four coders, according to behavioural learning theory, to sort techniques according to their mechanism of action. RESULTS: The panel (n = 35) agreed on 42 DBS techniques from a total of 63 candidate labels and descriptions. Complete agreement was achieved regarding all labels and descriptions, while agreement was not achieved regarding distinctiveness for 17 techniques. In exploring underlying principles of learning, it became clear that multiple and differing principles may apply depending on the specific context and procedure in which the technique may be applied. DISCUSSION: Experts agreed on what each DBS technique is, what label to use, and their description, but were less likely to agree on what distinguishes one technique from another. All techniques were describable but not comprehensively categorizable according to principles of learning. While objective consistency was not attained, greater clarity and consistency now exists. The resulting list of agreed terminology marks a significant foundation for future efforts towards understanding DBS techniques in research, education and clinical care.
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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 | 0.115 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.004 |
| 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".