Canada's Oral Health Workforce: Using Long-Run Trends to Inform Rising Demand for Dental Services
Bibliographic record
Abstract
BACKGROUND: In 2023, Canada launched a publicly funded dental care plan subsidizing coverage to nine million previously uninsured individuals. This sudden expansion of coverage has raised concerns about whether there is sufficient capacity to meet demand for services. OBJECTIVE: To contextualize the current and future capacity of dental services in Canada. METHODS: This study uses a quantitative analysis of longitudinal data (1997-2023) from Statistics Canada's Labour Force Survey (LFS) to examine trends in employment levels, hours worked, and earnings for occupations in the oral health workforce. RESULTS: While the dentists-to-population ratio remained relatively stable over the past two decades, similar ratios for dental assistants and hygienists increased. Average weekly work hours for assistants and hygienists were relatively steady, whereas dentists' hours declined in the first half of the period. From 1997 to 2023, wages for assistants and hygienists grew roughly in line with inflation, lagging behind both sectoral and national real wage growth over this period. CONCLUSIONS: Canada's relatively low dentist-to-population ratio, geographic maldistribution of dentists, and slower real wage growth-particularly among assistants-may pose challenges to meeting growing labour demand for oral health personnel.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".