Twelve tips for using the Objective Structured Teaching Exercise for faculty development
Bibliographic record
Abstract
BACKGROUND: The importance of faculty development to improve clinicians' teaching skills has been well articulated in the literature. There are few objective measures of the impact of faculty development on teaching skills. The objective structured teaching exercise (OSTE) is a faculty development tool that may meet this challenge. It also has great potential to be used in the development and enhancement of teaching skills. The OSTE consists of a simulated teaching scenario involving a standardized learner with objective and immediate feedback given to the teacher, and includes a pre-determined behaviourally based scale or checklist to assess teaching performance. AIM: There is little information in the literature on the practical aspects of how to develop and deliver an OSTE in a faculty development context. Based on our experience, we created a framework to guide the use of the OSTE for faculty development. METHODS: Twelve tips for using the OSTE for faculty development are outlined in this article. These include: clarifying the goal and target audience, identifying what teaching skills to focus on, developing the scenario and the assessment tool, choosing and training the standardized learner, holding a dry run, protecting the teacher, integrating the OSTE into one's own context and promoting buy-in, and evaluating the activity. CONCLUSIONS: The OSTE is a novel tool to enhance faculty development. We describe 12 key elements that are important for its successful development and delivery.
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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 | 0.029 | 0.074 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".