Person-Centred Dentistry: When Do We Start?
Bibliographic record
Abstract
Objectives: Although most health professions have adopted person-centred approaches in the last decades, dental professionals still rely on traditional paternalistic models. The objective of this research was to develop a model of person-centred care in dental practice.Methods: Adopting an action-research approach, we started our process by adapting Stewart and colleagues' model of person-centred care to the field of dentistry. We then implemented and tested the new clinical approach in a private dental office of Montreal, Canada. During several months, an academic researcher observed the clinical activities of a dental practitioner, who was also the first researcher. At the end of each appointment, the observer and the practitioner shared their observations about the encounter and evaluated the clinical approach. Both suggested improvements to the model and implemented solutions to the next patients. After 39 rounds of observation-evaluation-improvement, a form of saturation was reached in the development of the clinical approach.Results: The clinical encounter goes through four different stages. The first stage is how the relationship is built through an open dialogue on illness, fears and expectations. The second stage consists in gathering clinical information through examination, tests and questioning. The third stage is a disease-centered presentation of the results of the examination. The final stage is the development of a treatment plan after establishing a common ground through frank discussions and shared decision making. From this approach, a model emerged consisting in a stage of understanding, followed by planning, intervention, then back to understanding. This cycle takes place within the context of a patient-dentist relationship with mutual trust. This relationship, central to the encounter, acknowledges the whole-person dimension and psychosocial context of each party.Conclusions: We hope this model will inspire other professionals and teaching institutions to develop their own approaches by using it as a theoretical framework.
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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 | 0.088 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.010 | 0.032 |
| Scholarly communication | 0.022 | 0.046 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.016 | 0.030 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".