Implementing the Eat, Sleep, Console Model of Care: A Scoping Review
Bibliographic record
Abstract
OBJECTIVE: The Eat, Sleep, Console (ESC) model of care is an innovative care approach for infants diagnosed with neonatal abstinence syndrome, improving patient and health system outcomes for this equity-deserving population. Little is known about sustainably implementing this model into practice. The objective is to map evidence on implementing the ESC model into clinical practice, including strategies, barriers and facilitators to implementation, and evaluation outcomes. METHODS: Data sources include MEDLINE, Embase, CINAHL, PsycINFO, Google Scholar, and websites identified by a Google search. The study selection included articles exploring the implementation or evaluation of the ESC model in clinical practice since its 2017 conception. Two reviewers independently screened each study using a predetermined screening tool. Data were extracted by 2 independent reviewers from included articles. RESULTS: The review identified 34 studies. Barriers to implementing the ESC model include resource limitations and systemic oppression and bias. Facilitators include health care provider education and empowerment of parent engagement. The most reported cluster of strategies (31.6%) included training and educating stakeholders. Gaps were noted in the exploration of implementation outcomes/processes, and equity implications on implementation. CONCLUSIONS: The ESC model of care has been successfully implemented in various settings with positive patient and health system outcomes, including decreased hospital stay and pharmacological treatment of infants. However, there is a gap in exploring implementation processes and outcomes. Future research should explore the contextual elements of the implementation by equitably examining implementation outcomes specific to the ESC model of care.
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 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 | 0.029 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".