Imbalances in the oral health workforce: a Canadian population-based study
Bibliographic record
Abstract
BACKGROUND: In Canada, a new federal public dental insurance plan, being phased in over 2022-2025, may help enhance financial access to dental services. However, as in many other countries, evidence is limited on the supply and distribution of human resources for oral health (HROH) to meet increasing population needs. This national observational study aimed to quantify occupational, geographical, institutional, and gender imbalances in the Canadian dental workforce to help inform benchmarking of HROH capacity for improving service coverage. METHODS: Sourcing microdata from the 2021 Canadian population census, we described workforce imbalances for three groups of postsecondary-qualified dental professionals: dentists, dental hygienists and therapists, and dental assistants. To assess geographic maldistribution relative to population, we linked the person-level census data to the geocoded Index of Remoteness for all inhabited communities. To assess gender-based inequities in the dental labour market, we performed Blinder-Oaxaca decompositions for examining differences in professional earnings of women and men. RESULTS: The census data tallied 3.4 active dentists aged 25-54 per 10,000 population, supported by an allied workforce of 1.7 dental hygienists/therapists and 1.6 dental assistants for every dentist. All three professional groups were overrepresented in heavily urbanized communities compared with more rural and remote areas. Almost all dental service providers worked in ambulatory care settings, except for male dental assistants. The dentistry workforce was found to have achieved gender parity numerically, but women dentists still earned 21% less on average than men, adjusting for other characteristics. Despite women representing 97% of dental hygienists/therapists, they earned 26% less on average than men, a significant difference that was largely unexplained in the decomposition analysis. CONCLUSIONS: Accelerating universal coverage of oral healthcare services is increasingly advocated as an integral, but often neglected, component toward achieving the health-related Sustainable Development Goals. In the Canadian context of universal coverage for medical (but not dentistry) services, the oral health workforce was found to be demarcated by considerable geographic and gendered imbalances. More cross-nationally comparable research is needed to inform innovative approaches for equity-oriented HROH planning and financing, often critically overlooked in public policy for health systems strengthening.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".