A Review of Pediatric Dentistry Program Websites: What Are Applicants Learning About Our Programs?
Bibliographic record
Abstract
The purpose of this study was twofold: 1) to examine website content provided by U.S. and Canadian pediatric dentistry residency programs, and 2) to understand aspects of program websites that dental students report to be related to their interests. Sixty-eight program websites were reviewed by five interprofessional evaluators. A thirty-six-item evaluation form was organized into 1) program descriptive items listed on the American Academy of Pediatric Dentistry (AAPD) website (n=21); 2) additional program descriptive items not listed on the AAPD website but of interest (n=9); and 3) items related to website interface design (n=5). We also surveyed fifty-four dental students regarding their interest in various aspects of program descriptions. The results of this study suggest that pediatric dentistry residency programs in general tend to provide identical or less information than what is listed on the AAPD website. The majority of respondents (76 percent) reported that residency program websites would be their first source of information about advanced programs. The greatest gap between the available website information and students' interests exists in these areas: stipend and tuition information, state licensure, and program strengths. Pediatric dentistry residency programs underutilize websites as a marketing and recruitment tool and should incorporate more information in areas of students' priority interests.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".