Frayme: Building the structure to support the international spread of integrated youth services
Bibliographic record
Abstract
AIM: Frayme is a Canadian-based international network designed to accelerate the adoption and scaling up of integrated youth services (IYS). This is done through the synthesis of evidence from a variety of sources and a commitment to integrated knowledge mobilization (KMb) to inform research policy and practice. Frayme is utilizing innovative approaches to stakeholder engagement (youth, families, policy makers, funders, researchers and practitioners) and KMb in order to co-design system change. The purpose of this article describes the overall Frayme strategy and presents findings from a participatory needs assessment implemented to inform policy-related priorities. METHODS: The Frayme leadership team facilitated a participatory needs assessment with major stakeholder groups that applied a modified problem-solving activity. The needs assessment was on a designed to support diverse stakeholder perspectives on ways to improve knowledge mobilization of IYS. Qualitative data were analysed using a thematic analysis. RESULTS: The four themes identified through the needs assessment were: (a) traditional scientific practices, (b) organizational obstacles, (c) change aversion, and (d) pre-established stakeholder hierarchies. CONCLUSIONS: Through the recognition of these challenges, Frayme has developed a set of major objectives to inform projects, opportunities for knowledge sharing, implementation of evidence and scaling up of efforts. The Frayme integrated KMb model represents a unique applied example of an evidence-informed approach to practice collaboration in KMb to promote system change. The findings from this research also contribute to the expanding knowledge base with regard to complex evaluation and system transformation.
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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 | 0.032 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".