What's the CATCH? A Participatory Learning Model for Case Review Rounds to Support a Culture of Safety and Quality Improvement in Pediatric Hospital Medicine
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effectiveness of a new model, Case Analysis and Translation to Care in Hospital (CATCH), for the review of pediatric inpatient cases when an adverse event or "close call" had occurred. STUDY DESIGN: The curricular intervention consisted of an introductory podcast/workshop, mentorship of presenters, and monthly CATCH rounds over 16 months. The study was conducted with 22 pediatricians at a single tertiary care center. Intervention assessment occurred using participant surveys at multiple intervals: pre/post the intervention, presenter experience (post), physicians involved and mentors experience (post), and after each CATCH session. Paired t-tests and thematic analysis were used to analyze data. Time required to support the CATCH process was used to assess feasibility. RESULTS: Our overall experience and data revealed a strong preference for the CATCH model, high levels of engagement and satisfaction with CATCH sessions, and positive presenter as well as physicians-involved and mentor experiences. Participants reported that the CATCH model is feasible, engages physicians, promotes a safe learning environment, facilitates awareness of tools for case analysis, and provides opportunities to create "CATCH of the Day" recommendations to support translation of learning to clinical practice. CONCLUSIONS: The CATCH model has significant potential to strengthen clinical case rounds in pediatric hospital medicine. Future research is needed to assess the effectiveness of the model at additional sites and across medical specialities.
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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 | 0.049 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".