Cannabis use and suicidal ideation among youth: Can we democratize school policies using digital citizen science?
Bibliographic record
Abstract
BACKGROUND: School policies and programs are important in preventing Cannabis use among youth. This study uses an innovative digital citizen science approach to determine the association between Cannabis use and suicidal ideation among youth while investigating how school health policies mediate this association. METHODS: The study engaged 818 youth (aged 13-18 years) and 27 educators as citizen scientists via their own smartphones. Youths responded to time-triggered validated surveys and ecological momentary assessments to report on a complex set of health behaviours and outcomes. Similarly, educators' reported on substance misuse and mental health school policies and programs. Multivariable logistic regression modeling and mediation analyses were employed. RESULTS: 412 youth provided data on substance misuse and suicidal ideation. Cannabis use and other factors such as bullying, other illicit drug use, and youth who identified as females or other gender were associated with increased suicidal ideation. However, school policies and programs for substance misuse prevention did not mediate the association between Cannabis use and suicidal ideation. CONCLUSIONS: In the digital age, it is critical to reimagine the role of schools in health policy interventions. Digital citizen science not only provides an opportunity to democratize school policymaking and implementation processes, but also provides a voice to vulnerable youth.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".