Serious Illness Conversation–Evaluation Exercise: A Novel Assessment Tool for Residents Leading Serious Illness Conversations
Bibliographic record
Abstract
Background/Objectives: The serious illness conversation (SIC) is an evidence-based framework for conversations with patients about a serious illness diagnosis. The objective of our study was to develop and validate a novel tool, the SIC-evaluation exercise (SIC-Ex), to facilitate assessment of resident-led conversations with oncology patients. Design: We developed the SIC-Ex based on SIC and on the Royal College of Canada Medical Oncology milestones. Seven resident trainees and 10 evaluators were recruited. Each trainee conducted an SIC with a patient, which was videotaped. The evaluators watched the videos and evaluated each trainee by using the novel SIC-Ex and the reference Calgary-Cambridge guide (CCG) at months zero and three. We used Kane's validity framework to assess validity. Results: Intra-class correlation using average SIC-Ex scores showed a moderate level of inter-evaluator agreement (range 0.523–0.822). Most evaluators rated a particular resident similar to the group average, except for one to two evaluator outliers in each domain. Test–retest reliability showed a moderate level of consistency among SIC-Ex scores at months zero and three. Global rating at zero and three months showed fair to good/very good inter-evaluator correlation. Pearson correlation coefficients comparing total SIC-Ex and CCG scores were high for most evaluators. Self-scores by trainees did not correlate well with scores by evaluators. Conclusions: SIC-Ex is the first assessment tool that provides evidence for incorporating the SIG guide framework for evaluation of resident competence. SIC-Ex is conceptually related to, but more specific than, CCG in evaluating serious illness conversation skills.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".