Canadian resources on cannabis use and fertility, pregnancy, and lactation: A Scoping Review (Preprint)
Bibliographic record
Abstract
BACKGROUND Cannabis use among reproductive-aged Canadians is increasing, but our understanding of its impacts on fertility, pregnancy, and breastmilk is still evolving. Despite the availability of many online resources, informed-decision making and patient counselling are challenging for expectant families and providers alike. OBJECTIVE To conduct a scoping review of publicly available online Canadian resources providing information on the effects of cannabis on fertility, pregnancy, and breastmilk. METHODS We systematically searched eight databases between 1-Jan-2010 and 30-Nov-2020 and webpages of 71 Canadian obstetrical, government, and public health organizations. English resources discussing the effects of cannabis on fertility, pregnancy, breastfeeding, or the exposed fetus/infant were included. Epidemiological characteristics, readability, and content information were extracted and summarized. RESULTS A total of 183 met our inclusion criteria. Resources included content for public audiences (89.1%) and healthcare providers (n=31, 16.9%). The majority (68.3%) were written at a 10+ reading-grade level, and few (3.8%) were available in languages other than English or French. Common themes included citing a need for more research into the effects of cannabis on reproductive health and recommending that patients avoid or discontinue use. Although resources for providers were consistent in recommending patient counselling, resources targeting the public were less likely to encourage seeking advice from healthcare providers (n=23, 14.1%). CONCLUSIONS Canadian resources consistently identify that there is no known safe amount of cannabis that can be consumed in the context of fertility, pregnancy, and breastfeeding. Areas of improvement include increasing readability, language accessibility, and encouraging bidirectional communication between healthcare providers and patients. INTERNATIONAL REGISTERED REPORT RR2-10.1136/bmjopen-2020-045006
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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 | 0.012 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.030 | 0.056 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".