Optimizing the Impact of Public-Academic Partnerships in Fostering Policymakers’ Use of Research Evidence: Proposal to Test a Conceptual Framework
Bibliographic record
Abstract
BACKGROUND: Previous research has reported that public-academic partnerships (PAPs) can effectively promote PAP leaders' use of research evidence in improving youth outcomes. However, the existing literature has not yet clarified whether and how PAP leaders' use of research evidence evolves along the PAP life cycle and whether PAP partners' concordant perceptions of usefulness of their PAP has an impact on PAP leaders' use of research evidence. Developing a conceptual framework that recognizes the PAP life cycle and empirically identifying contexts and mechanisms of PAPs that promote PAP leaders' use of research evidence from the PAP life cycle perspective are imperative to guide researchers and policymakers to successfully lead PAPs and foster policymakers' use of research evidence for improving youth outcomes. OBJECTIVE: Utilizing an integrated framework of organizational life cycle perspective, a social partnership perspective, and a realist evaluation, this study examines the extent to which PAP development and PAP leaders' use of research evidence can be characterized into life cycle stages and identifies PAP contexts and mechanisms that explain the progress of PAPs and PAP leaders' use of research evidence through life cycle stages. METHODS: Recruiting PAPs across the United States that aim to improve mental health and promote well-being of youth aged 12-25 years, the study conducts a document analysis and an online survey of PAPs to inform policymakers and academic researchers on the contexts and mechanisms to increase PAP sustainability and promote policymakers' use of research evidence in improving youth outcomes. RESULTS: Fifty-three PAPs that meet the recruitment criteria have been identified, and document review of PAPs and participant recruitment for the online survey of PAP experience have been conducted. CONCLUSIONS: This paper will help policymakers and researchers gain a deeper knowledge of the contexts and mechanisms for each PAP life cycle stage in order to optimize PAP leaders' use of research evidence in achieving positive youth outcomes. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/14382.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
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 | 0.124 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.008 | 0.040 |
| Scholarly communication | 0.022 | 0.030 |
| Open science | 0.005 | 0.022 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".