Demographic Landscape and Practice Patterns of Canadian Orthodontists
Bibliographic record
Abstract
Purpose: To obtain information on the current demographic landscape and practice patterns of orthodontists in Canada. Methods: Dental regulatory authorities were contacted and surveyed on number, sex, dental school, graduate orthodontic program, years of dentistry and specialty graduation, and location of practice of licensed orthodontists. Information on professional memberships, fellowship, and board certification status was collected from the affiliated resources. The number and distribution of orthodontists was compared on a regional and population level with the aid of Statistics Canada records. Practice patterns were identified for licensed orthodontists in regards to sex, educational training, work experience, practice location, fellowship status, board certification, and professional memberships. Census divisions were extrapolated from practice locations to illustrate the spatial distribution of orthodontists, and statistical analysis identified associations between practice patterns and demographic and social factors. Results: Number: 932 orthodontists were identified (2.4 per 100,000 population) with 48 practitioners registered in more than one jurisdiction. Sex: 35% of orthodontists in Canada are currently female with the proportion of female graduates increasing over time. Training: 73% of orthodontists graduated from a Canadian dental school, while 63% graduated from a Canadian orthodontic specialty program. Certification: 57% of orthodontists were fellows of the RCDC and 9% were board certified with the ABO. Professional Membership: 53% of orthodontists were members of the CAO and 44% were members of the AAO. Experience: The mean years of experience was 18.5 with a median year of graduation of 2005. Location: Most orthodontists in Canada practiced out of one location (72%). Although considerable regional variation was noted with the distribution of orthodontists in Canada, a greater number of orthodontists practiced in regions with higher population densities and household incomes. Rural regions remained relatively underserved. Conclusions: This study is the first of its kind to characterize the demographics of orthodontists in Canada. This information will enable professional associations, graduate training programs, dental regulatory authorities, and government policymakers to better understand the orthodontic workforce to the benefit of the profession and the public.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 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".