Professional identity formation: The key contributors and dental students’ concerns
Bibliographic record
Abstract
OBJECTIVES: This study aimed to explore the components of professional identity formation (PIF) and understand dental students' concerns toward their professional identity development so that research-informed recommendations can be made to improve dental professional programs. METHODS: This is a qualitative study. A total of 18 students of the whole graduating class (class size: 46) were interviewed about their progress through a newly designed curriculum specific for the dental students at a large public research university in Canada. The audio files were recorded, transcribed, and corrected by a research assistant. Using QSR International's NVivo (Version 12), the researchers of this study conducted a thematic analysis to generate overarching themes and extract the relevant components of PIF. RESULTS: Five themes emerged from the study as follows: (i) domain-specific self-efficacy, (ii) role modeling and mentoring, (iii) professional socialization with peers, (iv) learning environment (LE), and (v) reflection. We considered these to be the five key contributors to dental students' PIF. CONCLUSIONS: Understanding the main concerns for students and improving the LE are critical in helping students form their professional identity. The findings of this qualitative study identified some important aspects of the dental curricula for educators to consider. These results can be used by future research studies to explore models for professional identity assessment tools that can aid in guiding students' professional identity 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 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| 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".