How do dental professionals perceive sustainable dentistry? A qualitative study
Bibliographic record
Abstract
BACKGROUND: Dentistry must reduce its ecological footprint in response to the triple ecological disaster of climate change, biodiversity loss, and pollution. It is thus necessary to introduce or reinforce sustainability in dental curricula and continuing education programmes. As such, this study aimed to explore how dental professionals perceive ecological issues, conceptualize and integrate sustainable practices into clinical routines, and identify their needs regarding continuing education in this domain. METHODS: We conducted a qualitative, exploratory study in partnership with the Ministry of Health and professional dental organizations of the province of Québec, Canada. We held six virtual focus groups (60-90 min) with a sample of 28 Québec practitioners-8 dentists, 9 dental hygienists, and 11 dental assistants-from public and private clinics. Sessions were audio-recorded, transcribed verbatim, and analyzed thematically following Braun and Clarke's approach. RESULTS: Most participants expressed concern about climate change and a sense of responsibility toward future generations. They defined sustainability as minimizing ecological impact without compromising infection control, but acknowledged a lack of information on the subject, often equating it with waste reduction while overlooking higher-impact sources such as patient and staff travel. Their self-initiated actions, which were shaped by their professional roles, faced barriers including time pressure, hierarchical decision-making, the cost of sustainable products, and infection control rules, operating at individual, clinic, and policy levels. All groups actively wanted continuing education on sustainable dentistry, preferably in a hybrid format, and requested clear clinical protocols along with supportive procurement and regulatory policies to make change feasible. CONCLUSIONS: Our analysis met the objectives by describing clinicians' perceptions and routines, identifying needs foental professionals expressed concerns aboutr continuing education, and surfacing multilevel barriers and enablers. Accordingly, it is important to develop continuing education programmes on sustainable dentistry that are tailored to their learning preferences and professionals' roles within the clinic. Training should be complemented by the provision of clear clinical protocols, designated sustainability leads, and procurement systems that remove cost and supply barriers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".