Desistance, Self‐treatment, or Substitution: Decisions about Cannabis Use During Pregnancy
Bibliographic record
Abstract
Cannabis is the most commonly used drug during pregnancy in the United States and Canada, and the American College of Obstetricians and Gynecologists recommends that all pregnant individuals be screened for cannabis use and counseled regarding potential adverse health impacts of use. However, those considering or using cannabis during pregnancy report experiencing stigma and lack of information from health care providers and, thus, frequently rely on friends, family, and the internet for information. This article describes 3 types of decisions individuals may be making about cannabis use during pregnancy and suggests approaches health care providers may take to minimize judgment and provide optimal support for informed cannabis use decisions among pregnant individuals. Desistance decisions involve consideration of whether and how to reduce or stop using during pregnancy. Self-treatment decisions are made by those exploring cannabis to help alleviate troublesome symptoms such as nausea or anxiety. Substitution decisions entail weighing whether to use cannabis instead of another substance with greater perceived harms. Health care providers should be able to recognize the various types of cannabis use decisions that are being made in pregnancy and be ready to have a supportive conversation to provide current and evidence-based information to individuals making desistance, self-treatment, and substitution decisions. Individuals making desistance decisions may require support with potential adverse consequences such as withdrawal or return of symptoms for which cannabis was being used, as well as potentially navigating social situations during which cannabis use is expected. Those making self-treatment decisions should be helped to fully explore treatment options for their symptoms, including evidence on risks and benefits. Regarding substitution decisions, health care providers should endeavor to help pregnant individuals understand the available evidence regarding risks and benefits of available options and be open to revisiting the topic over time.
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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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".