Teaching Observation as a Faculty Development Tool in Medical Education: A Scoping Review
Bibliographic record
Abstract
INTRODUCTION: Health professions education often includes teaching observation to inform faculty development (FD) and indirectly improve student performance. Although these FD approaches are well received by faculty, they remain underused and/or underreported, with limited opportunities to receive feedback in workplace contexts. The goal of our study was to map the depth and breadth of education literature on the use of observation of teaching as a tool of professional development in medical education. METHODS: Following the methodology by Arksey and O'Malley, we conducted a scoping review and searched four databases for articles published in English (final searches in April 2022). RESULTS: Of 2080 articles identified, 45 met the inclusion criteria. All observation activities were associated with one of the following FD approaches: peer observation of teaching (23 articles, 51%), peer coaching (12, 27%), peer review (9, 20%), and the critical friends approach (1, 2%). Thirty-three articles (73%) concerned formative versions of the observation model that took place in clinical settings (21, 47%), and they tended to be a voluntary (27, 60%), one-off (18, 40%), in-person intervention (29, 65%), characterized by limited institutional support (13, 29%). Both barriers and challenges of teaching observation were identified. DISCUSSION: This review identified several challenges and shortcomings associated with teaching observation, such as inadequate methodological quality of research articles, inconsistent terminology, and limited understanding of the factors that promote long-term sustainability within FD programs. Practical strategies to consider when designing an FD program that incorporates teaching observation are outlined.
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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 | 0.051 | 0.179 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.024 | 0.028 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".