Assessing graduating dental students’ competencies: the impact of classroom, clinic and externships learning experiences
Bibliographic record
Abstract
OBJECTIVES: The present study assessed recent dental graduates' educational experiences with regard to competency development in different learning contexts and preparedness for independent professional performance. METHODS: The present study employed a questionnaire examining University of Manitoba graduating dental students' confidence and perceived importance of 47 competencies expected by the ACFD/CDA by requiring students to rate each competency on a five-point Likert scale. In addition, contribution of each of the three learning environments (classroom, clinic, and externship) towards competency development was assessed. RESULTS: Recent graduates reported most confidence in areas of basic clinical procedures involving radiographic, pharmacologic and caries management, with least confidence in implantology, orofacial pain, trauma and surgical management. Most importance was attributed to interpersonal-communication and basic clinical skills, with least importance in scientific research, implantology and prosthetic laboratory aspects. Overall, graduates felt that clinical setting contributed the most to competency development, followed by classroom and then externship contexts. CONCLUSION: Graduating students' professional preparedness can reflect the quality of dental programme. However, the amount of importance that graduates place on each competency might impact their confidence in the associated competencies and vice versa. In addition, learning settings must be effectively utilised for particular competencies' 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".