Implementation and Evaluation of the Eat, Sleep, Console Model of Care for Babies Diagnosed with Neonatal Abstinence Syndrome: A Scoping Review Protocol
Bibliographic record
Abstract
Introduction: Infants diagnosed with neonatal abstinence syndrome (NAS) or neonatal opioid withdrawal syndrome (NOWS) constitute a growing population in Canada. In most facilities, an outdated model of care is used to guide the care and assessment of babies diagnosed with NAS. Challenges with this outdated model have prompted the transition to a novel approach to care, the Eat, Sleep, Console model. Despite this promising intervention to improve patient and health system outcomes, little is known on how to effectively implement and evaluate the model in clinical practice. Objectives: We will conduct a scoping review to address the question, “How has the Eat, Sleep, Console model been implemented and evaluated in practice?”. Methods: We will follow the JBI methodology for scoping reviews and Arksey and O’Malley’s scoping review framework. Reporting will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). Published and unpublished literature will be included in the review. The following databases and grey literature will be searched: MEDLINE, Embase, CINAHL, PsycInfo, Google Scholar, and websites identified in a Google website search. Two independent reviewers will screen literature and extract data based on predetermined eligibility criteria and data extraction tools. We will narratively describe quantitative data, along with completing an inductive thematic analysis of qualitative findings. Furthermore, we will conduct a directed content analysis of qualitative findings using the COM-B model of behaviour and RE-AIM (reach, effectiveness, adoption, implementation, and maintenance) framework. We anticipate findings will be used to support future implementation of the Eat, Sleep, Console model into clinical practice, including subsequent evaluation of implementation.
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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 | 0.147 | 0.128 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.016 | 0.018 |
| Bibliometrics | 0.020 | 0.015 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.011 | 0.006 |
| Insufficient payload (model declined to judge) | 0.050 | 0.010 |
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".