Unveiling the implicit : exploring professionalism and professional identity formation in dentistry
Bibliographic record
Abstract
Background: Professionalism in healthcare encompasses clinical competence, effective communication, ethical understanding, and humanistic attributes. It is a key component in dental education mandated by accreditation bodies in Canada and the U.S. However, the lack of a universally agreed-upon definition, inconsistent teaching approaches, and role modelling make it difficult to implement. Additionally, competitive market pressures and shifting social and economic priorities further challenge efforts to instill professionalism, even among experienced practitioners. Professional identity formation (PIF), which emphasizes the process of "becoming" a professional, may offer a potential pathway for fostering consistent and enduring professionalism in oral healthcare practitioners. Objectives: This dissertation explores key factors shaping PIF and its connection to professionalism among dental students and practicing dentists. It further seeks to offer insights into actionable recommendations for enhancing professionalism education through the lens of PIF. Methods: Surveys and semi-structured interviews were conducted with dental students and practicing dentists. Data analysis included descriptive statistics, thematic analysis, template analysis, and axiological analysis. Results: While professionalism is seen as essential, some participants questioned whether values like empathy, honesty, and respect can be fully taught. Key influences on student PIF included domain-specific self-efficacy, role modelling, peer interaction, the learning environment, and reflection. Students recommended embedding professionalism more fully across the curriculum, improving assessment, and promoting professional culture among faculty. For practitioners, PIF was shaped by work experience, personality and upbringing, education, and the title of “doctor.” However, findings suggested a potential disconnect between professional identity and ethical conduct, suggesting that experience alone, without deliberate reflection, may not ensure ethical behaviour. Dentists also noted gaps in ethics and interprofessional education, recommending enhanced professionalism training, stronger institutional oversight, and supportive work environments. Conclusion: While professionalism is highly valued, cultivating core values such as empathy, honesty, and respect may require a more comprehensive approach beyond traditional instruction, which often emphasizes knowledge acquisition and technical skills over the deeper, reflective aspects of professional identity. Integrating professionalism education throughout the curriculum and positioning PIF as a central goal could support ethical and humanistic development.
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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 | 0.020 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.010 | 0.022 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".