An environmental scan of impacts and interventions for women with methamphetamine use in pregnancy and their children
Bibliographic record
Abstract
BACKGROUND: Indigenous women are overrepresented among people who use (PWU) methamphetamine (MA) due to colonialism and intergenerational trauma. Prenatal methamphetamine exposure (PME) is increasing as the number of PWUMA of childbearing age grows. Yet impacts of MA in pregnancy and effective interventions are not yet well understood. OBJECTIVE: We conducted an environmental scan of published and grey literature (2010-2020) to determine effects of MA use in pregnancy for mothers and their offspring, effective interventions and implications for Indigenous women. SEARCH STRATEGY: A strategic search of Ovid Medline, Embase, ProQuest-Public Health and CINAHL databases identified academic literature, while Google and ProQuest-Public Health identified grey literature. SELECTION CRITERIA: Article selection was based on titles, abstracts and keywords. The time frame captured recent MA composition and excluded literature impacted by coronavirus disease 2019. DATA COLLECTION AND ANALYSIS: Data extracted from 80 articles identified 463 results related to 210 outcomes, and seven interventions. Analysis focused on six categories: maternal, neonatal/infant, cognitive, behavioral, neurological, and interventions. MAIN RESULTS: Maternal outcomes were more congruent than child outcomes. The most prevalent outcomes were general neonatal/infant outcomes. CONCLUSION: A lack of Indigenous-specific research on PME and interventions highlights a need for future research that incorporates relevant historical and sociocultural contexts.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".