Interprofessional Care Models for Pregnant and Early-Parenting Persons Who Use Substances: A Scoping Review
Bibliographic record
Abstract
Introduction: Use of substances during pregnancy is a global health concern. Interprofessional care teams can provide an optimal care approach to engage individuals who use substances during the perinatal period. The purpose of this scoping review is to provide a comprehensive summation of published literature reporting on interprofessional care models for perinatal individuals who use substances. Methods: We conducted a systematic search for articles from health-related databases. The Preferred Reporting Items for Systematic Reviews for Scoping Reviews (PRISMA-ScR) was followed. Data were extracted and synthesized to identify the interprofessional care team roles, program and/or provider characteristics, and care outcomes of these models. Results: We screened 645 publications for full text eligibility. Eleven articles met full inclusion criteria and were summarized. Programs were built on co-location of services, partnership with other agencies, available group/peer support and approaches inclusive of cultural care, trauma informed care, and harm reduction principles. Discussion: There is growing evidence supporting integrated care models that are inclusive of relational care providers from multiple health care professions to achieve wraparound care. Conclusions: Many of the interprofessional care models studied have successfully blended social, primary, pregnancy, and addictions care. The success and sustainability of programs varies, and more work is needed to evaluate program and patient outcomes.
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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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".