Assessing Dental Students’ Readiness to Treat Populations That Are Underserved: A Scoping Review
Bibliographic record
Abstract
In North America, all dental schools have adopted some form of community-based dental education (CBDE) or service-learning, but little is known about the areas being researched and reported in published studies. The aim of this study was to conduct a scoping review to determine what areas of research had been conducted to determine the effects of CBDE on dental students' readiness to treat populations that are underserved. A systematic search of articles published in English or French since 2000 was performed on July 29, 2015, and combined quantitative and qualitative synthesis of data was conducted. Of the 32 studies evaluated, 23 (72%) were quantitative, seven (22%) were qualitative, and two were multi-method. The majority (66%) used self-report methods, most frequently surveys. Participants in 50% of the studies were fourth-year dental students; the others assessed third- and fourth-year (13%), first- and second-year (6%), and first-year (13%) students. Dentists were the participants in three studies (9%), with dentists and students in one study (3%). Either the types of populations receiving care were unspecified or four or more groups were pooled together in 25 studies (78%), while two focused on children, one on rural populations, one on elderly populations, two on persons with special health care needs, and one on low-income populations. The study areas were wide-ranging, but generally fell into three categories: student performance (37.5%), teaching approaches and evaluation methods (37.5%), and perceptions of CBDE (25%). This review identified many research gaps for determining whether students are prepared to treat populations that are underserved. The disparate nature of CBDE research demonstrates a compelling argument for determining elements that define student readiness to care for patients who are underserved and for research that includes the voices of patients, curriculum development, and more comprehensive and rigorous evaluation methodologies.
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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 | 0.031 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.018 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 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 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".