Dental antimicrobial stewardship: a qualitative study of perspectives among Canadian dentistry sector leaders and experts in antimicrobial stewardship
Bibliographic record
Abstract
Objectives: Dentistry is a significant contributor to the burden of antimicrobial overprescribing and hence to the global problem of antimicrobial resistance. However, antimicrobial stewardship in Canadian dentistry is nascent, with an acknowledged need for research and coordinated stewardship efforts. This study aimed to gain insights into the perspectives of Canadian dentistry sector leaders and experts on the main drivers of dental antibiotic overprescribing and potential stewardship strategies. Methods: Exploratory qualitative design. Data collection: four one-time, 1 h focus group discussions with 22 experts and stakeholders in antimicrobial stewardship in Canada, recruited through a mix of purposive and snowball sampling. Data analysis: inductive thematic analysis. Results: The analysis yielded five themes: outdated patterns; antimicrobials as a Band-Aid; fear and risk aversion; behavioural change; and why reinvent the wheel? Overprescription in dentistry stems primarily from a perpetuation of outdated prescribing patterns, ubiquitous use of antibiotics as a temporary solution, and an overly cautious antibiotic use by risk-averse providers. Stewardship strategies should be grounded on behavioural change (motivation, robust data and enactment of new behaviours) and may be modelled after tested medical interventions. Conclusions: This study presents a roadmap for behavioural change in dental antibiotic prescribing, and points to the fact that the success of a stewardship actionable plan for Canadian dentistry may depend more on concerted efforts for change than on the creation of novel strategies. Hence, contextualizing and testing medical stewardship programmes in Canadian dentistry may be effective in combatting antibiotic overprescription, thereby contributing to global efforts to reduce antimicrobial resistance.
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.024 | 0.011 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".