Becoming a dentist: faculty perceptions of student experiences with threshold concepts in a Canadian dental program
Bibliographic record
Abstract
BACKGROUND: In each discipline, there are moments where students "get stuck" in their education and/or training and are often unable to move forward. These moments may be caused by threshold concepts as they represent a portal that students must cross in order to become successful in their chosen profession. This study investigated the threshold concepts from the instructors' perspective that students must navigate as they transform from learners to dentists within a dental program. METHODS: Two focus groups with faculty members within the School of Dentistry, University of Alberta were completed in the fall of 2017. Focus groups explored the faculty's perception of the students' transition from learner to dentist, difficult moments in the program, and the students' ability to navigate the program successfully. RESULTS: A qualitative phenomenographic analysis of the faculty focus group transcripts identified four potential threshold concepts within the dental program: 1) dealing with the whole patient, 2) accountability, 3) that you may not know everything, and 4) problem solving and adapting during practice. CONCLUSION: This study demonstrates that there are concepts within a dental program that faculty believe students must navigate in order to transition from learner to dentist. These concepts may inform curriculum design as well as other disciplines in the health sciences.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".