Assessing patient safety competencies using Objective Structured Clinical Exams: a new twist on an old tool
Bibliographic record
Abstract
Despite the widespread attention to patient safety over the past 15 years, the subject continues to receive relatively little attention in undergraduate training for health professionals (eg, in medical and nursing schools). Recent advances such as the WHO curriculum guide1 and the Canadian Patient Safety Institute competency framework1 ,2 help to guide our teaching and learning. Furthermore, some schools have implemented patient safety curricula.3 ,4 However, evaluating the degree to which students attain these competencies remains in its infancy (‘On a scale of 1–5, rate how well you did X’), with all the limitations of self-assessment.5 In an effort to progress the field further, Ginsburg et al 6 describe the findings of a pilot that used the Objective Structured Clinical Exam (OSCE) to assess patient safety competence. The OSCE provides a mechanism to move beyond assessing a learner's knowledge to its application by allowing the learner to show how they approach a scenario in a simulated setting. As such, it has largely become the cornerstone for the assessment of skills such as history taking, physical examination and even hand hygiene. Ginsburg et al used the OSCE to assess sociocultural patient safety competencies, which is a novel application of this traditional tool. The authors created scenarios true to inpatient ward settings for the simulation, which they used to provide and report summative learner assessments. Although they note, and we agree, that a high stakes summative assessment in patient safety competencies may drive what is taught and learned, evidence suggests that students …
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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 | 0.055 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.003 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.009 | 0.018 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.008 |
| 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".