What influences attitudes toward professionalism in dental students?
Bibliographic record
Abstract
OBJECTIVES: To explore dental students' attitudes toward professionalism and the environmental, institutional, and student-related factors that may be associated with these attitudes. METHODS: A cross-sectional online survey, conducted in 2020, analyzed data from a convenience sample of undergraduate dental students at the Faculty of Dentistry, University of Toronto. Attitudes toward professionalism were assessed using Likert scale statements related to the American Dental Education Association professionalism values of "Fairness," "Responsibility," "Respect," and "Service-mindedness." Codes ranging from 1 to 5 were assigned for the different levels of agreement and an "attitudes toward professionalism score" (ATPS) was computed by summing the codes for all the statements. Greater agreement with the statements or a higher ATPS indicated more positive attitudes toward professionalism. Association of the ATPS with environmental, institutional, and student-related factors was investigated using non-parametric tests and linear regression. RESULTS: The survey yielded a response rate of 51.4% (n = 221). The majority of respondents agreed with all professionalism statements. Results showed that the ATPS was significantly associated with and decreased for students who viewed their future patients as consumers (β = -3.41, 95% confidence interval [CI]: -5.21, -1.60), experienced unprofessional faculty behavior (β = -2.45, 95% CI: -4.88, -0.01), and chose to pursue dentistry for financial benefit (β = -2.55, 95% CI: -4.63, -0.47). CONCLUSION: This sample of dental students generally had positive attitudes toward professionalism and numerous factors were associated with these attitudes. Enhancing the instruction and reinforcement of professional attitudes may be important to students' application of professionalism in decisions regarding clinical practice.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".