Building engagement to support adoption of community-based substance use prevention initiatives
Bibliographic record
Abstract
BACKGROUND: System-level approaches that target social determinants of health are promising strategies to support substance use prevention, holistic youth development and wellbeing. Yet, the youth services system is largely based on individual-focused programs that do not adequately account for social determinants of health and place the responsibility for wellness on the individual. There is a need to understand how to enhance adoption of complex system-level approaches that support comprehensive youth development. The Icelandic Prevention Model (IPM) represents a collaborative initiative that takes an ecological, system-level approach to prevent substance use and promote wellness in youth. This research was designed to examine key stakeholder perceptions to better understand social motivations and contextual complexities that influence stakeholder support to garner community-level adoption of the IPM in a rural Canadian community. METHODS: This research applies a case study approach using qualitative interviews to explore strategies to support uptake in the early stages of IPM adoption associated with developing community buy-in and acceptance. A thematic analysis was applied using QSR NVivo. RESULTS: Nine interviews were conducted with community partners leading the implementation of the IPM. Three over-arching themes emerged from the data: 1) Motivating influences 2) Strategies to develop buy-in, and 3) Resistance to the adoption of the IPM. Findings reflect issues that affect behaviour change in system transformation in general as well as upstream prevention and the IPM, in particular. CONCLUSIONS: The findings from this research describe critical insight derived from implementing community-driven initiatives that are designed to support health promotion. It contributes new scientific knowledge related to implementation of complex system-level innovations and practical information that is useful for communities interested in implementing the IPM or following similar approaches to prevent substance use.
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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 | 0.024 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".