Building effective public dental care programs: The critical role of implementation evaluation
Bibliographic record
Abstract
There are significant income-related inequities in oral health and access to oral health care. Public dental programs generally aim to increase access to oral health care for individuals with financial barriers through government payments for appointments. Low engagement from both oral health care providers and intended patients are common challenges in delivery of public dental programs, and are impediments to program impact and outcomes. Still, these programs rarely address the systemic issues that affect the experiences of intended users. This accentuates the importance of monitoring of program delivery to refine or adapt programs to better meet needs of service providers and users. As such, specifying program goals and developing a related monitoring strategy are critical as Canada begins to implement a national public dental program. Drawing on an example of a pediatric public dental program for children from low-income families or with severe disabilities in Ontario, Canada, this article illustrates how an implementation and evaluation framework could be applied to measure implementation and impact of the national program. The RE-AIM framework measures performance across five domains: (1) Reach, (2) Effectiveness (patient level), (3) Adoption, (4) Implementation (provider, setting, and policy levels), and (5) Maintenance (all levels). Given the disparities in oral disease and access to oral health care, the results can be used most effectively to adapt programs if relevant stakeholders participate in reviewing data, investigating quality gaps, and developing improvement strategies.
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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 | 0.544 | 0.524 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.014 | 0.017 |
| Scholarly communication | 0.034 | 0.024 |
| Open science | 0.010 | 0.021 |
| Research integrity | 0.008 | 0.017 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".