Evidence-based programming for vulnerable youth: Successes and challenges of implementing healthy relationships programs in diverse settings
Bibliographic record
Abstract
In recent years a variety of evidence-based programs have been developed to promote mental health and reduce violence among youth, including those considered to be the most at risk. However, simply providing evidence-based programming to settings that serve vulnerable youth does not ensure the efficacy of these programs because of the unique contextual factors, strengths, and needs of those youth and settings. There is often a disparity between the efficacy of a program identified in a research context and the effectiveness of a program in its application in real world settings. The purpose of this study was to explore this gap through investigating the successes and challenges of implementing healthy relationships programs (the HRP and HRP-E) in a variety of contexts where vulnerable youth receive support. These contexts included school systems, community mental health, the youth justice sector, and child welfare. Semi-structured interviews and implementation surveys were used. Thematic analysis was used to analyse qualitative data, and descriptive statistics were used for quantitative data. Through using a mixed-methods approach, the goal was to explore the experiences and perspectives of the communities in which the HRP and HRP-E were being implemented, with the ultimate goal of facilitating more effective programming and research in the future. The results of this study found that there are a variety of successes and challenges that are universal across contexts, as well as numerous outcomes unique to specific contexts. To organize the results of this study and embed the findings within implementation research, the Consolidated Framework for Advancing Implementation Science (CFIR) was used.
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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 | 0.209 | 0.211 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.005 | 0.029 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".