Canada’s First National Oral Health Research Strategy (2024–2030)
Bibliographic record
Abstract
Recent years have seen significant positive changes and developments in oral health-related policy and data on oral health and oral health care in Canada. Simultaneously, on the international stage, the momentum for oral health and related research continues to build. These changes have led to an initiative to create Canada's first National Oral Health Research Strategy (NOHRS), which was recently published by the Canadian Institutes of Health Research-Institute of Musculoskeletal Health and Arthritis (Allison and Rock 2024). In this communication, we describe the process that was used to undertake this work. We present the resulting guiding principles, the research priority areas, and the framework that emerged, which included 6 strategic priorities grouped into 3 themes: (A) Leading Issues: (1) access to care, (2) inequities, identities, and oral health; (B) Emerging Methods: (3) artificial intelligence, (4) omics; and (C) Overarching Approaches: (5) environmental sustainability, (6) knowledge mobilization and implementation science. In addition, NOHRS includes a series of proposed goals and a timeline over the coming years. The point is to encourage a broad range of individuals and groups of people to engage with this high-level strategy and create plans to implement it. This strategy directly answers the call by the World Health Organization for countries to establish a national oral health research strategy (World Health Organization 2024). We have engaged in an extensive, broad consultative process, resulting in a Canadian NOHRS that is tailored to the needs of our community. Its aim is to galvanize our community into action to address the priorities we have identified. By engaging in this process, we build upon multiple oral health-related initiatives in Canada and on the international stage. We hope to inspire and facilitate similar, much-needed work elsewhere.
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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 | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 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".