Exploring the value of structured narrative feedback within the Serious Illness Conversation-Evaluation Exercise (SIC-Ex): a qualitative analysis
Bibliographic record
Abstract
OBJECTIVES: The Serious Illness Conversation Guide (SICG) 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 Serious Illness Conversation-Evaluation Exercise (SIC-Ex), to assess resident-led conversations with patients in oncology outpatient clinics. DESIGN: Qualitative study using template analysis. SETTING: Three academic cancer centres in Canada. PARTICIPANTS: 7 resident physicians (trainees), 7 patients from outpatient cancer clinics, 10 preceptors (raters) consisting of medical oncologists, palliative care physicians and radiation oncologists. INTERVENTIONS: Each trainee conducted an SIC with a patient, which was videotaped. The raters watched the videos and evaluated each trainee using the novel SIC-Ex and the reference Calgary-Cambridge Guide (CCG) initially and again 3 months later. Two independent coders used template analysis to code the raters' narrative comments and identify themes/subthemes. OUTCOME MEASURES: How narrative comments aligned with elements of the CCG and SICG. RESULTS: Template analysis yielded four themes: adhering to SICG, engaging patients and family members, conversation management and being mindful of demeanour. Narrative comments identified numerous verbal and non-verbal elements essential to SICG. Some comments addressing general skills in engaging patients/families and managing the conversation (eg, setting agenda, introduction, planning, exploring, non-verbal communication) related to both the CCG and SICG, whereas other comments such as identifying substitute decision maker(s), affirming commitment and introducing Advance Care Planning were specific to the SICG. CONCLUSIONS: Narrative comments generated by SIC-Ex provided detailed and nuanced insights into trainees' competence in SIC, beyond the numerical ratings of SIC-Ex and the general communication skills outlined in the CCG, and may contribute to a more fulsome assessment of SIC 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 | 0.050 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".