The Pediatric Serious Illness Conversation Program: Understanding challenges and experiences for clinicians after advance care planning training
Bibliographic record
Abstract
OBJECTIVES: To explore experiences of pediatric clinicians participating in a serious illness communication program (SICP) for advance care planning (ACP), examining how the SICP supports clinicians to improve their communication and the challenges of implementing new communication tools into clinical practice. METHODS: A qualitative description study using individual interviews with a diverse group of pediatric clinicians who participated in 2.5-hour SICP training workshops at pediatric tertiary hospitals. Discussions were transcribed, coded, and arranged into overarching themes. Thematic analysis was conducted using interpretive description methodology. RESULTS: Fourteen clinicians from 2 Canadian pediatric tertiary hospital settings were interviewed, including nurses (36%), physicians (36%), and social workers (29%), from the fields of neonatology (36%), palliative care (29%), oncology (21%), and other pediatric specialties (14%). Key themes included specific benefits of SICP, with subthemes of connecting with families, increased confidence in ACP discussions, providing tools to improve communication, and enhanced self-awareness and self-reflection. A second theme of perceived challenges emerged, which included subthemes of not having the conversation guide readily accessible, divergent team communication practices, and particular features of the clinical environment which limited the possibility of engaging in ACP discussions with parents. SIGNIFICANCE OF RESULTS: A structured program to enhance serious illness communication supports clinicians to develop skills and tools to increase their confidence and comfort in conducting conversations about end-of-life issues. Addressing challenges of adopting the newly learned communication practices, by providing access to digital SICP tools and conducting SICP training for clinical teams may further support clinicians to engage in ACP.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".