Adaptation of Icelandic Model for Prevention of Adolescent Substance Use in New Brunswick
Bibliographic record
Abstract
The Icelandic Prevention Model (IPM) is a multi-level community collaborative initiative that focuses on broader social and environmental determinants over individual-based intervention, aiming to prevent substance use. The IPM is now being applied in other countries, and there is a need to explore implementation strategies to support adoption of the model in new settings. The objective of this research is to examine stakeholder perceptions of the implementation barriers and facilitators within the early stages of IPM implementation in New Brunswick, Canada. Semi-structured qualitative interviews and focus groups were held with key stakeholders (N = 35) and data were analyzed using QSR NVivo using thematic analysis. The results are categorized in two over-arching themes focused on barriers and facilitators. We identified themes related to barriers, including partnership challenges, maintaining sustained engagement over the long-term and lack of community engagement and buy-in. Additional themes related to facilitators highlight components of the IPM that facilitate implementation, effective implementation strategies and building relationships in community. This paper offers new insights related to the implementation of the IPM in Canada, which will be valuable for other communities interested in implementing the model, as well as for future policy and practice in implementation of upstream prevention initiatives.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".