Guidelines: The do’s, don’ts and don’t knows of direct observation of clinical skills in medical education
Bibliographic record
Abstract
INTRODUCTION: Direct observation of clinical skills is a key assessment strategy in competency-based medical education. The guidelines presented in this paper synthesize the literature on direct observation of clinical skills. The goal is to provide a practical list of Do's, Don'ts and Don't Knows about direct observation for supervisors who teach learners in the clinical setting and for educational leaders who are responsible for clinical training programs. METHODS: We built consensus through an iterative approach in which each author, based on their medical education and research knowledge and expertise, independently developed a list of Do's, Don'ts, and Don't Knows about direct observation of clinical skills. Lists were compiled, discussed and revised. We then sought and compiled evidence to support each guideline and determine the strength of each guideline. RESULTS: A final set of 33 Do's, Don'ts and Don't Knows is presented along with a summary of evidence for each guideline. Guidelines focus on two groups: individual supervisors and the educational leaders responsible for clinical training programs. Guidelines address recommendations for how to focus direct observation, select an assessment tool, promote high quality assessments, conduct rater training, and create a learning culture conducive to direct observation. CONCLUSIONS: High frequency, high quality direct observation of clinical skills can be challenging. These guidelines offer important evidence-based Do's and Don'ts that can help improve the frequency and quality of direct observation. Improving direct observation requires focus not just on individual supervisors and their learners, but also on the organizations and cultures in which they work and train. Additional research to address the Don't Knows can help educators realize the full potential of direct observation in competency-based education.
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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 | 0.044 | 0.209 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.013 | 0.006 |
| Research integrity | 0.019 | 0.017 |
| Insufficient payload (model declined to judge) | 0.007 | 0.008 |
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".