Stigma Experienced by Rural Pregnant Women with Substance Use Disorder: A Scoping Review and Qualitative Synthesis
Bibliographic record
Abstract
Identification and recognition of experiences of rural pregnant women with substance use disorder is needed to inform public policy and medical training. This paper reviews and qualitatively synthesizes literature exploring the experiences of this population. Embase, PubMed, and Web of Science were used to identify literature through August 2022 using the search terms, such as pregnancy, substance use or abuse, stigma, and rural. Cited and citing research were also considered. Exclusion criteria included articles that failed to consider rural pregnant women's perspectives on stigma experienced, included potential confounds, occurred outside of the United States or Canada, and were published before January 2000. Nine articles met the inclusion criteria. Data were synthesized by the listed authors and assessed for common themes. A review of the articles revealed three common themes: stigma occurs in community settings, stigma occurs in healthcare settings, and comprehensive care is important to ensure appropriate support to this population. Stigma as a barrier seems to improve when women have strong social support and access to comprehensive care networks. Addressing this stigma through programs, such as peer social guidance and comprehensive health centers, may provide appropriate support to pregnant, rural women with SUD to further navigate their health needs.
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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 | 0.015 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".