Editorial Perspective: A call to collective action – improving the implementation of evidence in children and young people's mental health
Bibliographic record
Abstract
With growing mental health needs of children and young people and the increasing demand on children and young people's mental health services, narrowing the evidence to practice implementation gap has never been more important. Implementation science and research provides useful theory, identification of barriers and facilitators as well as suggested strategies for improved uptake of evidence-based treatments, but the application of these is often limited. Supporting optimal learning and implementation cultures based on collaborative, relational and pragmatic action planning is likely key. We propose suggested next steps and recommendations to move this agenda forward within the children and young people's mental health field with a 'call to action'. With the need for specific roles and clear accountability, we emphasise that between clinicians, researchers, consumers and policy makers this is everyone's business.
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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 | 0.025 | 0.080 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.019 | 0.011 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.040 | 0.037 |
| Insufficient payload (model declined to judge) | 0.018 | 0.017 |
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".