Multi-service prevention programs for pregnant and parenting women with substance use and multiple vulnerabilities: Program structure and clients’ perspectives on wraparound programming
Bibliographic record
Abstract
BACKGROUND: In Canada, several community-based, multi-service programs aimed at reaching vulnerable pregnant or parenting women with substance use and complex issues have emerged. These programs offer basic needs and social supports along with perinatal, primary, and mental health care, as well as substance use services. Evaluations of these 'one-stop' programs have demonstrated positive outcomes; nevertheless, few published studies have focused on how these programs are structured, on their cross-sectoral partnerships, and on clients' perceptions of their services. METHODS: The Co-Creating Evidence (CCE) project was a three-year evaluation of eight multi-service programs located in six Canadian jurisdictions. The study used a mixed-methods design involving semi-structured interviews, questionnaires, output data, and de-identified client data. This article focuses on qualitative interviews undertaken with 125 clients during the first round of site visits, supplemented by interview data with program staff and service partners. RESULTS: Each of the programs in the CCE study employs a multi-service model that both reflects a wrap-around approach to care and is intentionally geared to removing barriers to accessing services. The programs are either operated by a health authority (n = 4) or by a community-based agency (n = 4). The programs' focus on the social determinants of health, and their provision of primary, prenatal, perinatal and mental health care services is essential; similarly, on-site substance use and trauma/violence related services is pivotal. Further, programs' support in relation to women's child welfare issues promotes collaboration, common understanding of expectations, and helps to prevent child/infant removals. CONCLUSIONS: The programs involved in the Co-Creating Evidence study have impressively blended social and primary care and prenatal care. Their success in respectfully and flexibly responding to women's diverse needs, interests and readiness, within a community-based, wraparound service delivery model paves the way for others offering pre- and postnatal programming.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".