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 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 | 0.022 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".