Virtual and Interprofessional Objective Structured Clinical Examination in Dentistry and Dental Technology: Development and User Evaluations
Bibliographic record
Abstract
BACKGROUND: Interprofessional education (IPE) facilitates interprofessional collaborative practice (IPCP) to encourage teamwork among dental care professionals and is increasingly becoming a part of training programs for dental and dental technology students. However, the focus of previous IPE and IPCP studies has largely been on subjective student and instructor perceptions without including objective assessments of collaborative practice as an outcome measure. OBJECTIVE: The purposes of this study were to develop the framework for a novel virtual and interprofessional objective structured clinical examination (viOSCE) applicable to dental and dental technology students, to assess the effectiveness of the framework as a tool for measuring the outcomes of IPE, and to promote IPCP among dental and dental technology students. METHODS: The framework of the proposed novel viOSCE was developed using the modified Delphi method and then piloted. The lead researcher and a group of experts determined the content and scoring system. Subjective data were collected using the Readiness for Interprofessional Learning Scale and a self-made scale, and objective data were collected using examiner ratings. Data were analyzed using nonparametric tests. RESULTS: We successfully developed a viOSCE framework applicable to dental and dental technology students. Of 50 students, 32 (64%) participated in the pilot study and completed the questionnaires. On the basis of the Readiness for Interprofessional Learning Scale, the subjective evaluation indicated that teamwork skills were improved, and the only statistically significant difference in participant motivation between the 2 professional groups was in the mutual evaluation scale (P=.004). For the viOSCE evaluation scale, the difference between the professional groups in removable prosthodontics was statistically significant, and a trend for negative correlation between subjective and objective scores was noted, but it was not statistically significant. CONCLUSIONS: The results confirm that viOSCE can be used as an objective evaluation tool to assess the outcomes of IPE and IPCP. This study also revealed an interesting relationship between mutual evaluation and IPCP results, further demonstrating that the IPE and IPCP results urgently need to be supplemented with objective evaluation tools. Therefore, the implementation of viOSCE as part of a large and more complete objective structured clinical examination to test the ability of students to meet undergraduate graduation requirements will be the focus of our future studies.
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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 | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".