Using a composite morbidity score and cultural survey to explore characteristics of high proficiency neonatal intensive care units
Bibliographic record
Abstract
BACKGROUND: Continuous quality improvement (CQI) collaboration has not eliminated the morbidity variability seen among neonatal intensive care units (NICUs). Factors other than inconstant application of potentially better practices (PBPs) might explain divergent proficiency. OBJECTIVE: Measure a composite morbidity score and determine whether cultural, environmental and cognitive factors distinguish high proficiency from lower proficiency NICUs. DESIGN/METHODS: Retrospective analysis using a risk-adjusted composite morbidity score (Benefit Metric) and cultural survey focusing on very low birth weight (VLBW) infants from 39 NICUs, years 2000-2014. The Benefit Metric and yearly variance from the group mean was rank-ordered by NICU. A comprehensive survey was completed by each NICU exploring whether morbidity variance correlated with CQI methodology, cultural, environmental and/or cognitive characteristics. RESULTS: 58 272 VLBW infants were included, mean (SD) age 28.2 (3.0) weeks, birth weight 1031 (301) g. The 39 NICU groups' Benefit Metric improved 40%, from 80 in 2000 to 112 in 2014 (P<0.001). 14 NICUs had composite morbidity scores significantly better than the group, 16 did not differ and 9 scored below the group mean. The 14 highest performing NICUs were characterised by more effective team work, superior morale, greater problem-solving expectations of providers, enhanced learning opportunities, knowledge of CQI fundamentals and more generous staffing. CONCLUSION: Cultural, environmental and cognitive characteristics vary among NICUs perhaps more than traditional CQI methodology and PBPs, possibly explaining the inconstancy of VLBW infant morbidity reduction efforts. High proficiency NICUs foster spirited team work and camaraderie, sustained learning opportunities and support of favourable staffing that allows problem solving and widespread involvement in CQI activities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".