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 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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".