Sex and Gender Influences on Problematic Cannabis Use and Cannabis Use Disorder: A Scoping Review Protocol
Bibliographic record
Abstract
ABSTRACT Introduction Cannabis use and cannabis use disorder (CUD) are more prevalent among men and boys than among women and girls. However, this sex/gender gap has been narrowing in recent decades, likely due to an increase in cannabis use among women and girls, who have been historically under-represented in cannabis research. The lack of sex- and gender-based approaches within cannabis research has been highlighted in previous reviews, some of which have synthesized existing literature of associations between sex, gender, and cannabis use. What is missing is a clinically-relevant synthesis of evidence for sex and gender influences on problematic cannabis use, including treatment-related outcomes that could be used to influence care. The objective of this scoping review is to identify and synthesize published evidence about the influence of sex and gender on correlates and outcomes of treatment among people with problematic cannabis use (including CUD). Furthermore, we will examine to what extent this published literature has considered how sex and gender intersect with other social categories such as race and sexuality. Methods and Analysis This scoping review will follow the most commonly used methodology, the 2005 Arksey and O’Malley scoping study framework (including the optional consultation exercise to solicit feedback from relevant stakeholders) and the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) Extension for Scoping Reviews reporting guidelines. We will search MEDLINE, Embase, CINAHL, PsycINFO, and Web of Science for articles published between 2010 and the present. Included studies must be conducted in human participants with problematic cannabis use (e.g., diagnosis or screening for CUD) and include an analysis of sex- and/or gender-related factors. Using Covidence software, two independent reviewers will screen each record at the title/abstract and full text phases. Two independent reviewers will then use a data charting form developed by the study team to extract data. Data charting and both phases of article screening will begin with a pilot process completed by the entire team to ensure consistency. Article data will be exported into a spreadsheet to facilitate summary and basic descriptive statistics. Studies will be grouped together first by content area (e.g., treatment correlates, treatment effectiveness), then by study design, and which sex- or gender-related factors are considered in the analysis. Dissemination We will disseminate findings using two main strategies. First, we will engage in traditional knowledge translation, including publication in peer-reviewed journals and presentation at both medical and scientific conferences. Second, we will engage in knowledge translation strategies that will reach a wider audience (e.g., presentations to non-researcher audiences, dissemination of findings through social media networks, and development of brochures, infographics, and short videos to summarize our findings for a lay audience). We aim to ultimately engage relevant stakeholders (including clinicians) to determine how the identified evidence can best support care of problematic cannabis use.
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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 | 0.085 | 0.089 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.015 | 0.015 |
| Bibliometrics | 0.024 | 0.016 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.067 | 0.011 |
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".