Prerequisites in Behavioral Sciences and Business Revisited a Decade Later
Bibliographic record
Abstract
Introduction: Ten years ago a study reported on the lack of prerequisites in behavioral sciences and business management for entry into dental college. The primary objective of this follow-up study was to assess the current status of similar prerequisite courses in both U.S. and Canadian dental schools. Materials and Methods: Required and recommended courses in behavioral sciences and in business in each North American dental school’s official online publication were analyzed in December 2019. Admissions requirements were readily available in sixty-five of the sixty-eight U.S. dental schools and in all ten schools in Canada; three U.S. schools were contacted by phone to collect the information. Result: Only nine (13.2%) U.S. and four (40%) Canada schools required at least one behavioral sciences course; however, thirty-nine (57.3%) U.S. and six (60%) Canada schools recommended a behavioral sciences course, most often in psychology, sociology, or communication. Zero (0%) of the total seventy-eight dental schools required a course in business; however, eighteen (26.5%) U.S. and two (20%) Canada schools recommended a business course, most often economics. Conclusion: The authors strongly encourage the dental education community to again reconsider establishment of a minimum core of behavioral sciences and business courses as prerequisites in predoctoral dental education. Doing so will provide a much needed stronger foundation upon which to build upon in the curricula of dental schools.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 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".