Increasing Support for Opioid Exposed Newborns: A Quality Improvement Project
Bibliographic record
Abstract
Problem Statement Opioid exposed newborns, who may develop neonatal abstinence syndrome, benefit from non-pharmacological care, such as holding or “cuddling”. It is difficult for parents and staff at University of Vermont Children’s Hospital to provide adequate cuddling to fulfill all opioid exposed newborns’ needs. This project aimed to increase access to cuddling for opioid exposed newborns in the newborn nursery of University of Vermont Children’s Hospital so that 50% of newborns assessed to benefit from cuddling were cuddled by March 1st, 2020. Methods Quality improvement methodology was employed - including identifying aims, measures, and ideas for change - by conducting stakeholder interviews and creating a process map and key driver diagram. Ideas for change were implemented through “plan do study act” cycles. A mixed methods approach was used to analyze quantitative and qualitative data from the pre-intervention and post-intervention period. Descriptive data will be provided for each outcome, process, and balancing measure. Results Baseline nurse survey estimates indicated only 8.7% of opioid exposed newborns assessed to benefit from cuddling accessed cuddling in the pre-intervention period. Less than one quarter of nurses reported ever using cuddlers, most needs were overnight, and most nurses predicted using cuddlers would decrease their workload. At one month, there were 53 cuddling sessions reported and 11 volunteers cuddled in the newborn nursery. Results of follow up survey are pending. Conclusion As a result of this project, initial data may indicate increased access to cuddling for the opioid exposed infants. Most volunteers report neutral or satisfied with the program, although there are often no newborns to cuddle in the nursery during current shifts. Over the coming months, we anticipate expanding the hours to match unit needs, branching out to inpatient pediatrics to help with cuddling and decrease overlap, and training more volunteers.
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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 | 0.044 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".