Disclosure of Adverse Events - An OSCE Series for OB/GYN Residents
Bibliographic record
Abstract
Abstract Disclosing adverse events to patients after a poor outcome is an essential task involving both communication skills and professionalism. Assessing an individual resident's ability to perform a disclosure meeting during a clinical rotation is important, as it ensures clinical competency and is essential in minimizing medico-legal risk. However, disclosures may be difficult to teach and assess during clinical rotations. This opportunity may not arise during the rotation, and an attending physician may not be present during the occasion whereby the resident performs an act of disclosure. Furthermore, the attending staff may not be consistent in their expectations of what constitutes an adequate disclosure meeting. Objective structured clinical exams (OSCEs) can allow an opportunity to assess these skills. An OSCE station with a standardized patient (SP) allows an opportunity to evaluate these skills. The objective of this resource is to assess residents' communication skills involving the disclosure of a poor outcome to a SP using a patient encounter OSCE. Three OSCEs are presented here, with the intention that they be used as a pretest, a posttest after formal instruction in disclosure, and a delayed posttest to assess for long-term learning. In the first two stations, the disclosure OSCE is paired with, and preceded by, a counseling OSCE station. The residents are evaluated using guidelines for the disclosure of adverse events developed by the Canadian Patient Safety Institute and published by the Canadian Medical Protective Association. These OSCEs have been used successfully at our institution to assess disclosure, and are the subject of two scholarly papers (pending publication) on disclosure.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".