Examining the implementation of the Icelandic model for primary prevention of substance use in a rural Canadian community: a study protocol
Bibliographic record
Abstract
Abstract Background The Icelandic Prevention Model (IPM) is a collaborative upstream model that was designed to influence risk and protective factors related to substance use within the community, school, peer and family contexts. By engaging whole communities, the IPM has been found to be effective in reducing youth substance use behaviours across Iceland. As an extension to the IPM’s participatory approach, this research will examine how youth involvement can enhance outcomes. In addition, this research will evaluate whether the IPM approach is beneficial for mental health promotion and general youth wellbeing. Methods The present research protocol applies the bioecological model within a participatory mixed-method case study design to examine the implementation of the IPM in a rural community in Canada. This study was designed to identify whether the Icelandic substance use prevention model is effective in reducing substance use and promoting mental health and development for Canadian youth. It will also explore how to engage youth within the approach and how this adaptation influences implementation and outcomes. Discussion The findings from this study will contribute to our understanding of upstream prevention of youth substance use and will be used to support scaling of the IPM across Canada.
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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 | 0.039 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.017 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.030 | 0.004 |
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".