Does competition affect the clinical decision‐making of dentists? A geospatial analysis
Bibliographic record
Abstract
OBJECTIVES: To investigate the association between dentists' geographic density and perceptions of market competition with clinical decision-making among a representative sample of dentists in Ontario, Canada's most populated province. METHODS: Competition was quantified using dentist density, defined as the number of dental clinics lying within a one kilometre radius around the respondents' clinic address and by self-reported perceived pressure from other dental clinics. The outcome (clinical decision-making or treatment intensity) was measured using a set of clinical scenarios, which categorized dentists as either relatively aggressive or conservative. Associations were assessed using bivariate analysis and logistic and linear regression. RESULTS: Dentists who perceived large competitive pressure from other dentists (OR = 1.63, 95% CI: 1.07-2.49) were relatively more aggressive in their treatment choices. Interestingly, dentists located in very low dentist density areas (OR = 1.31, 95% CI: 1.03-1.68) were also relatively more aggressive in their treatment choices. CONCLUSION: This study is the first to explore the impact of competition on the clinical decision-making of dentists in a Canadian context. It presents a valuable addition to the competition literature and helps to understand current dynamics in the Canadian dental care market.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".