Impact of Sociodemographic Factors on the Distribution of Orthodontists
Bibliographic record
Abstract
Purpose: To identify the location of orthodontists and orthodontist service sites (OSSs) in Canada, to classify these OSSs by modality, and determine which, if any, sociodemographic factors may predict where orthodontists choose to practice. Methods: Dental regulatory authorities in Canada were contacted to obtain information on the practicing orthodontists in each jurisdiction. The full postal code for each OSS in Canada was obtained and each location was classified by practice modality and Forward Sortation Area (FSA). Sociodemographic variables of interest by FSA were extracted from Statistics Canada 2021 Census data, which included population, education, income, ethnocultural characteristics, and household characteristics. Correlations and associations between sociodemographic variables and practice locations by FSA were assessed. A binomial logistic regression was used to determine which explanatory variables were most predictive of the presence of orthodontist in an FSA. Results: There were found to be 1,181 OSSs in Canada, the majority being orthodontic specialty offices (74.8%). Several of the explanatory variables studied were found to have a weak or moderate correlation with the number of orthodontists and OSSs in an FSA, including Total Population (r = 0.45); the Proportion of Individuals with a Bachelor’s Degree or Higher (r = 0.33); the Median Value of Dwellings (r = 0.35); the Proportion of Immigrants (r = 0.38); and the Proportion Married or Living Common Law (r = -0.20). There was a statistically significant association between the presence of an orthodontist in an FSA, and whether the FSA was categorized as urban or rural (p < 0.001). Population, education, income, ethnocultural, and household characteristic variables were significantly associated with the presence of an orthodontist in an FSA, although the specific explanatory variables were not identical in urban and rural areas. In rural communities Total Population, and the Percentage of Households Earning $100,000 and Over were most predictive of an orthodontist being present in an FSA, explaining 25.3% of the variance in the final logistic model. In urban communities Total Population, Median Age of the Population, Proportion of Individuals with a Bachelor’s Degree or Higher, Median Value of Dwellings in an Area, and Household Size were most predictive of an orthodontist being present in an FSA explaining 37.1% of the variance in the final logistic model. Conclusions: This study is the first of its kind to provide information about the practice landscape of orthodontic offices in Canada, and the relationship between sociodemographic factors and orthodontist practice locations. The results of this study can be used by residents, orthodontists, graduate programs, regulatory bodies, government and policy makers to better serve the profession and public. It may also aid orthodontists in identifying areas in Canada with favorable sociodemographic characteristics to set up an orthodontic practice.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.005 | 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".