Sex and Gender Influences on Problematic Cannabis Use and Cannabis Use Disorder: A Scoping Review Protocol
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,085 | 0,089 |
| Méta-épidémiologie (sens strict) | 0,005 | 0,006 |
| Méta-épidémiologie (sens large) | 0,015 | 0,015 |
| Bibliométrie | 0,024 | 0,016 |
| Études des sciences et des technologies | 0,005 | 0,005 |
| Communication savante | 0,008 | 0,009 |
| Science ouverte | 0,006 | 0,007 |
| Intégrité de la recherche | 0,008 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,067 | 0,011 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».