P0304 / #439: A NOVEL INTERVENTION TO SUPPORT STAFF IN THE PEDIATRIC INTENSIVE CARE UNIT - SCHWARTZ CENTER ROUNDS
Bibliographic record
Abstract
Aims & Objectives: Moral distress (MD) is a concern for all practitioners in the pediatric intensive care unit(PICU). Though MD is well-described in the literature, interventions to address it remain under-studied. Our primary goal was to evaluate the impact of a novel implementation of Schwartz Center Rounds (SCR), a reoccurring multidisciplinary forum where health care providers discuss issues arising during clinical care. Our secondary goal was to describe the adaptation of SCRs within our setting. Methods: Setting: academic PICU from 2016 to 2019. Participants included physicians, nurses, and support staff. We performed a mixed-model analysis. Participant experience was measured by a survey that included yes/no/not sure options and open-ended questions. We performed a thematic analysis of survey comments, panelist feedback, and committee notes. Results: 99 surveys from eight SCRs were completed. 100% rated the sessions as good or excellent and 94.9% planned to attend again. 99.0% believed sessions provided new insight into perspectives and experiences of their co-workers and 80.6% new insight into those of patients and/or families. 77.6% of participants felt less isolated in their work with patients because of SCRs. 59.4% felt better prepared to handle tough or sensitive patient situations, though 33.3% were not sure. The most frequent theme encountered was an increased awareness and sensitivity among participants for their colleagues’ experiences. Conclusions: The adaptation of SCRs in the PICU was well-rated, perceived to provide insight into patients/families and co-worker perspectives and experiences, and led to feeling less isolated. Further study is needed to evaluate additional outcomes (e.g., clinical practice changes, team communication/collaboration).
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".