Youth substance use service provider’s perspectives on use and service access in ontario: time to reframe the discourse
Bibliographic record
Abstract
Abstract Background Age is a critical factor in substance use and related outcomes, with adolescence being a particularly sensitive period. Early initiation of substance use has been linked with higher risk for developing substance use disorders. In Ontario, Canada, substance use is common among youth, yet treatment is underutilized, suggesting the potential for an unmet need in terms of substance use care. Despite these challenges, there is limited research examining factors that contribute to youth substance use and youth-specific barriers to substance use care. To fill this knowledge gap, this study sought to include the unique perspectives of service providers who work directly with youth to examine these issues. Methods We used a cross-sectional mixed-methods design to examine factors that contribute to substance use among youth and identify youth-specific barriers to substance use among a sample of 54 Ontario-based youth service providers. Data collection included an online survey completed by all study participants followed by qualitative interviews of a subsample of 16 participants. Data analysis included basic frequency tabulations for survey results and thematic qualitative analyses to identify common themes. Results Licit substances were identified as the most commonly used among youth, where 94% of respondents identified cannabis use and 81% identified alcohol use. Thematic analyses identified the role of dominant substance use discourses in normalizing certain substances (i.e., cannabis and alcohol) while also endorsing stigmatizing beliefs and sentiments. According to youth service providers, the intersection of these two discourses simultaneously lead to an increase in substance use while deterring youth from seeking substance use care. Conclusions Normalization and stigmatization are two dominant discourses around youth substance use, with important implications for public health interventions. Key public health strategies, as identified by participants, to reduce the overall negative effect of these factors include the need to reframe substance use discourse, from a moral failing to a public health issue and to educate youth about the impacts of use. To accomplish this goal educational campaigns to raise awareness around the health effects of use and address stigmatization are needed. Educational reforms are also needed to ensure that these programs are integrated into the school system.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.021 | 0.014 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".