University-based initiatives towards better access to oral health care for rural and remote populations: A scoping review
Bibliographic record
Abstract
This scoping review maps a wide array of literature to identify academic programs that have been developed to enhance oral health care for rural and remote populations and to provide an overview of their outcomes. Arksey and O'Malley's 5-stage scoping review framework has steered this review. We conducted a literature search with defined eligibility criteria through electronic databases, websites of academic records, professional and rural oral health care organizations as well as grey literature spanning the time interval from the late 1960s to May 2017. The charted data was classified, analyzed and reported using a thematic approach. A total of 72 citations (67 publications and seven websites) were selected for the final review. The review identified 62 universities with program initiatives towards improving access to oral health care in rural and remote communities. These initiatives were classified into three categories: training and education of dental and allied health students and professionals, education and training of rural and remote community members and oral health care services. The programs were successful in terms of dental students' positive perception about rural practice and their enhanced competencies, students' increased adoption of rural practices, non-dental health care providers' improved oral health knowledge and self-efficacy, rural oral health and oral health services' improvement, as well as cost-effectiveness compared to other strategies. The results of our review suggest that these innovative programs were effective in improving access to oral health care in rural and remote regions and may serve as models for other academic institutions that have not yet implemented such programs.
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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 | 0.020 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.019 | 0.019 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".