Strategies to improve delivery of equitable and evidence-informed care for pregnant and birthing people with a substance use disorder in acute care settings: A scoping review protocol
Bibliographic record
Abstract
This protocol outlines a proposed scoping review to characterize evidence on implementation and quality improvement (QI) strategies that aim to improve equitable, evidence-informed care delivery for pregnant and birthing people with substance use disorder (SUD) in acute care. Untreated SUD during pregnancy is associated with an increased risk of overdose and severe maternal morbidity. Acute care settings are one important place to deliver equitable, evidence-informed clinical care. While clinical practice guidelines for substance use treatment and care of pregnant and birthing people with SUD exist, there are gaps in implementation. Our population of interest is pregnant and birthing people with SUD in an acute care setting. We will include US-based studies that describe or evaluate implementation or QI strategies, including experimental, observational, and descriptive studies published from 2016 to 2023. The proposed scoping review will be conducted in accordance with JBI methodology for scoping reviews and registered at OSF (registration number: BC4VZ). We will search MEDLINE (PubMed), CINAHL Complete (EBSCO), Scopus (Elsevier), and APA PsychInfo (Ovid) for published studies. Conference proceedings and Perinatal Quality Collaborative websites will be searched for grey literature. Two reviewers will independently screen then extract studies that meet inclusion criteria using a data extraction tool. The completion of this scoping review will help illuminate strengths and gaps in research and practice that aim to inform substance use treatment and care in acute care settings for pregnant and birthing people with SUD.
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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 | 0.241 | 0.185 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.009 | 0.015 |
| Bibliometrics | 0.022 | 0.021 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.010 | 0.015 |
| Research integrity | 0.012 | 0.008 |
| Insufficient payload (model declined to judge) | 0.054 | 0.018 |
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".