Comparison of the distribution of the dental hygienist workforce and population in Ontario: a geospatial analysis
Bibliographic record
Abstract
OBJECTIVES: This study conducted a geospatial analysis of the distribution of the dental hygienist workforce relative to the distribution of the population in Ontario, Canada, aiming to address workforce imbalances and inform regional and international workforce planning. METHODS: Geospatial analysis techniques were employed to examine the dental hygienist workforce distribution using anonymized datasets from the College of Dental Hygienists of Ontario (the professional regulatory body) and the Canadian census. The data were linked using the forward sortation area (FSA) component of Canadian postal codes, covering 520 FSAs across Ontario. Analyses were conducted at three levels, based on different aggregations of postal code data. RESULTS: The study found significant variations in the distribution of dental hygienists across Ontario. The analysis revealed pockets of high dental hygienist density, mostly in urban areas, and areas with low dental hygienist rates, especially in rural and remote locations. The overall provincial rate was 97 dental hygienists per 100,000 population, with variation across the 520 FSAs, from zero to 20,000 dental hygienists per 100,000 population (or zero to 739 dental hygienists per 100,000 population if five outlier FSAs were removed). CONCLUSIONS: The study underscores the complexity of dental hygienist workforce distribution in Ontario, revealing significant geographical disparities that suggest areas of both oversupply and undersupply. These insights provide actionable guidance for workforce policies and regulatory strategies, such as targeted incentives and public health initiatives, to address the mismatch between workforce supply and population needs. The findings highlight the importance of regular geospatial analyses to track changes in workforce distribution over time. The rigorous methodological approach and comprehensive evaluation of potential limitations offer valuable guidance for similar analyses in other jurisdictions. By providing a detailed framework and insights that extend beyond Ontario, this study contributes to the global understanding of dental hygienist workforce dynamics and supports the development of informed policies on a broader scale.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".