Implications of time and space factors related with youth substance use prevention: a conceptual review and case study of the Icelandic Prevention Model being implemented in the context of the COVID-19 pandemic
Bibliographic record
Abstract
PURPOSE: This research examines the implementation of the Icelandic Prevention Model (IPM) in Canada to identify opportunities revealed by the COVID-19 pandemic to re-design our social eco-system to promote wellbeing. This paper has two objectives: 1) to provide a conceptual review of research that applies the bioecological model to youth substance use prevention with a focus on the concepts of time and physical space use and 2) to describe a case study that examines the implementation of the IPM in Canada within the context of the COVID-19 pandemic. METHOD: Study data were collected through semi-structured qualitative interviews with key stakeholders involved in implementing the IPM. RESULTS: Findings are organized within three over-arching themes derived from a thematic analysis: 1) Issues that influence time and space use patterns and youth substance use, 2) Family and community cohesion and influences on developmental context and time use and 3) Opportunities presented by the pandemic that can promote youth wellbeing. CONCLUSION: We apply the findings to research on the IPM as well as the pandemic to examine opportunities that may support primary prevention and overall youth wellbeing. We use the concepts of time and space as a foundation to discuss implications for policy and practice going forward.
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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".