Advancing the Measurement of Dental Students’ Professionalism
Bibliographic record
Abstract
In dental education, professionalism has been viewed as a requirement in order to achieve and maintain competence in the practice of dentistry. The Professionalism Mini-Evaluation Exercise (P-MEX), a 21-item instrument validated in medicine, is often used to measure the following observable professionalism behaviors: doctor-patient relationship, reflective skills, time management skills, and interprofessional relationship skills. Emotional intelligence (EI) is defined as the ability to accurately perceive emotions in oneself and in others in order to improve performance and personal growth. The primary aim of this study was to elucidate for dental education the relationship of professionalism as measured by the P-MEX to EI as measured by the Emotional Quotient Inventory (EQ-i) 2.0, and the secondary aim was to explore relationships between EQ-i 2.0 subscales and the P-MEX. A correlational cohort study was conducted in 2015-16 in which the EQ-i 2.0 was administered to dental students at one U.S. dental school at the end of their second year as they began their clinical education experience. Out of a total class of 66 students, 49 (74%) were chosen to participate through randomized selection in order to have about 12 students per team clinic group. The P-MEX evaluations were collected eight months later in three settings: the comprehensive care clinic, community outreach clinics, and clinical care seminars. The students' EQ-i 2.0 mean scores and P-MEX mean scores resulted in a non-significant correlation. However, the EQ-i 2.0 subscales self-actualization and happiness were significantly correlated with the P-MEX mean scores. These results suggest that there was a relationship between these students' EI and professionalism, which supports the use of both for the evaluation and development of professionalism through a multiple triangulated effort.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".