Under Oath: Content Analysis of Oaths Administered in ADA‐Accredited Dental Schools in the United States, Canada, and Puerto Rico
Bibliographic record
Abstract
This study reviews and analyzes the content of dental school oaths taken by students in the United States, Canada, and Puerto Rico in 2006. Each oath was qualitatively reviewed to determine its consistency with each of the five principles set forth in the American Dental Association (ADA)'s Principles of Ethics and Code of Professional Conduct. Fifty-eight oaths were received from sixty-one of sixty-six schools in response to information requests regarding use of oaths and manner of administration. Of these, thirty-nine employ one oath, administered at either graduation or ceremonies marking transition to clinical training; twelve employ an oath at both occasions, with five repeating the same oath; and ten have no formal oaths. Eighteen oaths follow the wording of "The Dentist's Pledge," nine follow the "Oath to the Profession/Professional Pledge," three follow the Modern Hippocratic Oath, and twenty-eight are idiosyncratic. All five of the ADA principles (autonomy, nonmaleficence, beneficence, justice, and veracity) are addressed in thirteen oaths, four principles in nine oaths, and three or fewer principles in thirty-six oaths. Eleven make reference to care for the underserved. As oath-taking is an opportunity to instill and reinforce to students dentistry's most important ethical obligations, recommendations are offered to make the content more meaningful and comprehensive.
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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 | 0.012 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".