Optimizing Implementation of the Neonatal Enhanced Recovery After Surgery Guideline
Bibliographic record
Abstract
INTRODUCTION: Enhanced Recovery After Surgery (ERAS®) protocols require multidisciplinary team engagement from healthcare professionals (HCPs), where limited studies exist on neonatal ERAS®protocols. Therefore, we aimed to capture perceptions of HCPs on facilitation and implementation of the neonatal ERAS®guideline. METHODS: 10 neonates were recruited. 13 HCPs involved in these patient's care were interviewed and 8 surveyed consisting of pediatric anesthesiologists, neonatologists, neonatal intensive care unit (NICU) registered nurses (RNs), and pediatric surgeons. Using a multi-methods design, recruitment, semi-structured interviews and surveys were conducted from May 17, 2021 to November 1, 2022. Data was coded using The Promoting Action on Research Implementation in Health Studies and then thematically analyzed. RESULTS: Interviews were conducted with 4 pediatric anesthesiologists, 4 neonatologists, 2 NICU RNs, and 3 pediatric surgeons and surveys with 1 pediatric anesthesiologist, 2 neonatologists, 3 NICU RNs, and 2 pediatric surgeons. From interviews, the top 3 facilitation strategies were utilization of: (1) multidisciplinary guideline champions, (2) reminders and education, and (3) results to facilitate adherence. Incorporation of these strategies resulted in perceived: (1) stronger buy-in and engagement and (2) improved team communication, job satisfaction, care quality, and parental involvement. CONCLUSION: HCPs stressed the importance of guideline champions, reminders and education, and results distribution. Given implementation during the COVID-19 pandemic, awareness and education were mixed. Nonetheless, HCPs perceived improved buy-in and engagement, communication, job satisfaction, quality of care, and parental involvement. Incorporation of these strategies can promote successful ERAS® guideline facilitation and implementation and should be considered for future ERAS® projects. LEVEL OF EVIDENCE: IV.
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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 | 0.017 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".