Foundation observation of teaching project – A developmental model of peer observation of teaching
Bibliographic record
Abstract
INTRODUCTION: Peer observation of teaching is important in the development of educators. The foundation curriculum specifies teaching competencies that must be attained. We created a developmental model of peer observation of teaching to help our foundation doctors achieve these competencies and develop as educators. METHODS: A process for peer observation was created based on key features of faculty development. The project consisted of a pre-observation meeting, the observation, a post-observation debrief, writing of reflective reports and group feedback sessions. The project was evaluated by completion of questionnaires and focus groups held with both foundation doctors and the students they taught to achieve triangulation. RESULTS: Twenty-one foundation doctors took part. All completed reflective reports on their teaching. Participants described the process as useful in their development as educators, citing specific examples of changes to their teaching practice. Medical students rated the sessions as better or much better quality as their usual teaching. DISCUSSION: The study highlights the benefits of the project to individual foundation doctors, undergraduate medical students and faculty. It acknowledges potential anxieties involved in having teaching observed. CONCLUSION: A structured programme of observation of teaching can deliver specific teaching competencies required by foundation doctors and provides additional benefits.
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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 | 0.030 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".