An illustrative guide to a Policy Lab model: contributing to evidence-informed policies for digital technology in youth mental health care
Bibliographic record
Abstract
Objective This article provides researchers with an illustrative guide for a workshop model that facilitates evidence-informed policy. The Policy Lab model supports collaboration between experts and policymakers; in the example provided, participants considered digital technologies with near-term potential to improve youth mental health care. Method The Policy Lab model uses structured workshop activities to explore a policy question, before narrowing the focus on potential answers. The barriers, enablers, and implementation mechanisms of potential policies are then considered. From this data policy proposal(s) are drafted, reviewed, and reported. Results Through the Policy Lab activities, participants identified two priority technologies and generated data to inform the formulation of two policies. The policies were focused on (1) using artificial intelligence to improve the personalisation and precision of youth mental health care and (2) the expanded use of integrated data to improve youth mental health service quality. Conclusions Evidence-informed policy is a collaborative process. To potentially influence policy requires timely engagement with policymakers and an understanding of the policy context. Researchers considering using the model are encouraged to include a range of expertise.
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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 | 0.061 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.016 | 0.013 |
| Insufficient payload (model declined to judge) | 0.020 | 0.009 |
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".