Education Initiative for Essential Conversations with Parents—Serious Illness Guide for Pediatrics (SICG-Peds©-Peds)
Bibliographic record
Abstract
Objectives To explore whether training on the SICG-Peds© (Serious Illness Guide for Pediatrics) impacts a pediatric clinician's confidence and knowledge in participating and initiating/leading difficult conversations with families of children living with a serious illness. Methods Over 2 years, clinicians from BC and Ontario who care for children with serious illnesses attended training workshops on the SICG-Peds©. These workshops were evaluated using pre- and post-workshop questionnaires. Participants reported confidence scores on a scale of 1 to 10. Results In total, 134 and 140 clinicians attended the workshops in BC and Ontario, respectively. The majority of clinicians who completed the immediate post-workshop survey ( N = 223, 94.5%), responded that the content enhanced their knowledge about pediatric serious illness conversations (SIC). Confidence scores in initiating/leading a SIC significantly increased from median 5 (interquartile range [IQR] 3-7) pre-workshop to 7 (IQR 7-9) immediately post-workshop ( P < .0001). Confidence scores in participating as a non-leader in a SIC also significantly increased from median 7 (IQR 6-9) pre-workshop to median 8 (IQR 7-9) immediately post-workshop ( P < .0001). Conclusions Overall, these workshops significantly improved participants’ confidence. Effectiveness and knowledge enhancement were other areas where the majority of participants reported positive improvements. Areas for continued support were identified as more mentoring, and training for supporting the guide's use in a clinical setting.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".