Taking stock of youth substance use portrayals: A critical content analysis of Canadian news media, 2016–2024
Bibliographic record
Abstract
The changing landscape of substance use and related harms, interventions, and priority setting in Canada has intensified public commentary about youth and drugs. Given the politicized nature of these issues and the significant role of media in shaping societal views and responses to substance use, there is pragmatic value in examining how youth substance use is represented in contemporary media coverage, including to identify potential shortcomings. This study employs a mixed-methods critical content and discourse analysis to explore the characteristics and consequences of youth substance use as portrayed in Canadian news media. Data comprise news articles (N = 611) published between 2016 and 2024 and referencing youth substance use, retrieved from Canadian Newsstream. Quantitative content analysis was used to collate information about the Types of Substances commonly referenced in the news media, as well as the Nature of the Problem, Solutions Proposed, and Experts Represented. This informed the qualitative content and discourse analysis, which surfaced key media problem representations related to youth substance use: Uncritical and Generalized Representations of Harms on the Rise, Insufficient Resources, and Youth's Lack of Agency. The analysis also distilled issues pertaining to the solutions proposed in the articles: Missing Youth Perspectives, Downstream Interventions, and Individualistic Solutions Devoid of Context. Together, the study findings explicate how contemporary news media is reflecting and, in turn, shaping public discourses about youth substance use. From these findings, we discuss opportunities to shift media and broader public discourse to more comprehensively frame and address youth substance 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 | 0.021 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.022 | 0.029 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.001 | 0.004 |
| 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".