Abstract P-058: INTEGRATED CARE TEAM CASE ROUNDS: ENHANCING THE TEAM APPROACH FOR COMPLEX CRITICALLY ILL CHILDREN
Bibliographic record
Abstract
Aims & Objectives: Inter-professional (IP) education can facilitate enhanced team function and learning. Performing well as an integrated care team is a continuous process requiring IP learning opportunities be embedded into systems and practice. This is challenging in the clinical setting. We will describe the development and early experiences of Integrated Care Team Case Rounds (ICT-CR) in a quaternary critical care program. Methods ICT-CR’s purpose is to promote IP knowledge-sharing in a case-based learning format. The IP team selects 2 cases each week, one case each from the cardiac and paediatric ICUs for a 45-minute, high-level facilitated discussion. Discussion goals are pre-articulated and facilitators circulate selected relevant materials beforehand. Weekly de-briefings of key participants are conducted to identity effective learning strategies and opportunities to improve. Results Refinement and articulation of the ICT-CR process was attained. ICT-CR are well attended with 40–50 individuals representing the range of health care professionals in critical care. Topics presented include chronic critical illness, team relationships and rehabilitation approaches. Occasional discipline-specific presentations that complement case content are provided. Discussions include generalizable educational and clinically relevant points for follow-up and integration into the child’s plan of care. Sessions are most productive when facilitators respect the established format, ensure safety for all participants and when partner teams actively participate. Conclusions ICT-CR are an established part of the critical care education program. Collaborative contributions of the entire team have achieved team learning, clinical goals and have enhanced IP team function.
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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 | 0.002 | 0.007 |
| 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 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".