“To smoke feels gender”: Exploring the transformative and emancipatory capacities of cannabis among transgender, non-binary and gender non-conforming (TGNC) youth
Bibliographic record
Abstract
BACKGROUND: Transgender, non-binary and gender non-conforming (herein, "TGNC") youth (15-24 years old) face overlapping minority stressors (e.g., gender discrimination, lack of access to gender-affirming care, rejection, violence) that contribute to mental health inequities. TGNC youth also use substances at higher rates when compared to cisgender youth, including some of the highest rates of cannabis use in Canada. METHODS: This community-based participatory research study provides an in-depth qualitative, photovoice-based analysis examining how cannabis use features within the gender experiences of a sample of TGNC youth in British Columbia (BC). We conducted in-depth, semi-structured interviews with 27 TGNC youth (15-24 years old) from across British Columbia. Interviews were designed to elicit discussions about the photos youth had taken as well as various gender and mental health experiences related to their cannabis use. Analysis and identification of emergent themes was guided by social constructivist grounded theory as well as queer and trans theorizing and informed by community-based research approaches through regular meetings with our team's Substance Use Beyond the Binary Youth Action Committee comprised of TGNC youth who use substances. RESULTS: Three overarching themes pertaining to cannabis use and gender experiences amongst TGNC youth in our study were generated. First, participants used cannabis purposefully and strategically to enact diverse gender expressions and embodiments. Second, participants leveraged cannabis to support introspection whilst mobilizing identity discovery and development. Finally, participants mobilized cannabis as a vehicle for accessing moments of gender euphoria and affirmation. CONCLUSIONS: These findings identify how some TGNC youth use cannabis to purposefully and strategically facilitate their mental health, well-being, identity development and self-expression. This research reveals critically important experiential and embodied dimensions of cannabis use that have not historically been considered in cannabis-related policy and the provision of care, including mental health and substance use-related care.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.012 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".