Assessing procedural skills in context: exploring the feasibility of an Integrated Procedural Performance Instrument (IPPI)
Bibliographic record
Abstract
BACKGROUND: The assessment of clinical procedural skills has traditionally focused on technical elements alone. However, in real practice, clinicians are expected to be able to integrate technical with communication and other professional skills. We describe an integrated procedural performance instrument (IPPI), where clinicians are assessed on 12 clinical procedures in a simulated clinical setting which combines simulated patients (SPs) with inanimate models or items of medical equipment. Candidates are observed remotely by assessors whose data are fed back to the clinician within 24 hours of the assessment. This paper describes the feasibility of IPPI. RESULTS: A full-scale IPPI and 2 pilot studies with trainee and qualified health care professionals has yielded an extensive data set including 585 scenario evaluations from candidates, 60 from clinical assessors and 31 from simulated patients (SPs). Interview and questionnaire data showed that for the majority of candidates IPPI provided a powerful and valuable learning experience. Realism was rated highly. Remote and real-time assessment worked effectively, although for some procedures limited camera resolution affected observation of fine details. DISCUSSION: IPPI offers an innovative approach to assessing clinical procedural skills. Although resource-intensive, it has the potential to provide insight into individual's performance over a spectrum of clinical scenarios and at no risk to the safety of patients. Additional benefits of IPPI include assessment in real time from experts (allowing remote rating by external examiners) as well as provision of feedback from simulated patients.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".