Health Problems Mediate the Effects of Adverse Childhood Experiences on the Frequency of Cannabis Use in a Sample of Pregnant and Breastfeeding Women
Bibliographic record
Abstract
Many health organizations recommend that mothers avoid cannabis during pregnancy and breastfeeding because they are concerned about exposing infants to Δ-9-tetrahydrocannabinol (THC), the psychoactive substance in cannabis. Yet, data collected by the U.S. Centers for Disease Control demonstrate that a small percentage of mothers continue to use cannabis despite warnings. The frequency of cannabis use is an important variable because frequent use increases THC exposure. The present study examined two variables related to the frequency of cannabis use during pregnancy and breastfeeding: health problems and adverse childhood experiences (ACEs). We examined a possible mediation effect of health problems on the relationship between ACEs and the frequency of cannabis use during pregnancy and breastfeeding. Our sample was entirely comprised of 1,343 women who used cannabis during pregnancy and breastfeeding. We collected data online. The women were recruited from a Facebook group that supports pregnant and breastfeeding mothers who use cannabis. To be included, participants needed to be at least 18 years old and to have used cannabis while pregnant or breastfeeding. The sample was 79% White, 8% Hispanic, and 14% Black, and 1,199 currently resided in the United States, 76 in Canada, 11 in the United Kingdom, and the rest resided in 13 other countries. Ninety-three percent of the sample reported at least one ACE, and 59% reported 4 or more. Ninety-six percent reported that they were using cannabis to treat a health problem, and the number of health problems ranged from 0 to 8. Two mediation analyses found that the total number of ACEs increased the risk of health problems, which increased the frequency of cannabis use. ACE total was not significantly related to the frequency of use once health problems were accounted for. ACEs are related to the frequency of cannabis use in pregnant and breastfeeding women, but indirectly through trauma's impact on health problems. These findings suggest that practitioners might be able to lower the frequency of cannabis if they directly address health problems.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".