Addressing Health Inequities in Cannabis Research: Developing an Inclusion Tool to Support the Production of Gender-Inclusive Public Health Research
Bibliographic record
Abstract
Using a critical discourse analysis (CDA) of the Canadian Centre on Substance Use and Addiction (CCSA) corporate reports, infographics, reports, and report summaries, the research examines the relationship between gender and cannabis consumption among Canadian youth. The CDA evaluates gender inclusion supported by the application of the gender inclusion scale (GIS). A total of 44 CCSA publications focused on cannabis consumption were scored on the GIS to assess the gender inclusion of CCSA research. The data highlighted apparent gender differences supporting the recognized need for gender inclusion in public health research. Gender plays a significant role in cannabis consumption; the CCSA research concludes that male cannabis consumers face elevated susceptibility to adverse health risks and detrimental harms associated with cannabis consumption. The heightened susceptibility to harm and risk correlated with male cannabis consumers is the product of (masculine) drug cultures, drug-taking risk behaviour influenced by gender roles, and gendered perceptions of risks related to drugs. The research recognizes the critical value of gender inclusion in public health research, developing a GIS Tool to provide a resource to employ gender-inclusive research production, GIS score identification, and identify areas where gender inclusion could be improved in effective incorporation within public health research. The GIS Tool is not limited to cannabis research but can be transferred to all public health research environments. The research proposes gender inclusion as a means to better safeguard Canadians, specifically Canadian youth, from cannabis-related disadvantageous risks and harms, calling for comprehensive gender-inclusive work to inform guiding Canadian policies and practices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".