Open-ended comments in SIC-Ex, an assessment tool for residents leading Serious Illness Conversations
Bibliographic record
Abstract
Abstract Purpose The Serious Illness Conversation (SIC) has emerged as a framework for conversations with patients with a serious illness diagnosis. This study reports on narratives generated from open-ended questions of a novel assessment tool, the SIC-Evaluation Exercise (SIC-Ex), to assess resident-led conversations with patients in oncology outpatient clinics.Methods We developed the SIC-Ex based on the Ariadne SIC framework. Seven resident trainees and ten preceptors were recruited from three cancer centres. Each trainee conducted a SIC with a patient, which was videotaped. The preceptors watched the videos and evaluated each trainee using the novel SIC-Ex and the reference Calgary-Cambridge Guide (CCG) at months 0 and 3. Two independent coders used template analysis to code the preceptors’ free-text narrative comments and identify themes/subthemes.Results Template analysis yielded 6 themes: behavioural attributes mapped to SIC, those mapped to CCG, those overlapping between SIC and CCG, trainees’ demeanors, rater mis-classification of comments, and comments on SIC-Ex. Narrative comments explored numerous verbal and non-verbal components essential to SIC. Some comments applied to both SIC and CCG (e.g. setting agenda, introduction, planning, exploring, non-verbal communication), whereas others mapped specific to one (e.g. SIC - identifying substitute decision maker, affirming commitment, introducing advance care planning, engaging family; CCG – using open ended questions, avoiding explanations, flow, time management).Conclusion Narrative comments generated by SIC-Ex provided a detailed and nuanced insight into trainee's competency in SIC, beyond numerical ratings and general communication skills assessed by CCG; they should continue to be a part of assessment.
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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 | 0.031 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.005 |
| 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".