Differentiating the Drug Normalization Framework: A Mixed Methods Investigation of Substance Use Among Undergraduate Students in Canada
Bibliographic record
Abstract
This study investigates substance use trends, norms, and the social integration of drugs among undergraduate students in Canada through application of the drug normalization framework. This framework is designed to assess shifts in recreational substance use patterns, attitudes, and practices. In this dissertation, I focus on two prominent psychoactive substances in the university context: (1) cannabis as the most commonly used illicit drug in Canada, and (2) the nonmedical use of prescription drugs commonly prescribed for Attention Deficit Disorder. In Chapter Two I provide a multivariate analysis of a survey of 1,713 undergraduate students attending university at three campuses in Canada: the University of Toronto, the University of Guelph, and the University of Alberta. This chapter provides insight into cannabis normalization as differentiated by social location predictors. It illustrates the gendered character of cannabis acceptability attitudes and use rates, and provides evidence for a substance use acculturation effect experienced by students who were born abroad. In addition, this chapter evidences a complex relationship of peer network cannabis use prevalence to cannabis acceptability attitudes, where high network prevalence is associated with lower acceptability attitudes than "some" network prevalence, indicating a threshold effect for cannabis acceptability. In Chapter Three I apply a "doing gender" analysis of in-depth semi-structured interviews with 58 students from the University of Toronto to investigate the salience of gendered norms and stigma with respect to cannabis use rates, accessibility, and acceptability attitudes. I show how the gendered differentiation of cannabis normalization remains significant despite gendered convergence in lifetime use rates of cannabis. In Chapter Four I investigate acceptability evaluations of the nonmedical use of prescription medications through analyzing a subset of 36 interviews with students from the University of Toronto. The findings of this chapter are used to critically engage with and expand the drug normalization framework's construct of drug acceptability. To conclude, I identify areas for future research and discuss the implications of these results in light of impending changes to drug policy in Canada which will render cannabis a legally regulated commercial product for recreational consumption.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".