Rethinking evidence-based practice for children’s mental health
Bibliographic record
Abstract
“Efficiency is concerned with doing things right. Effectiveness is doing the right things.” Drucker, 1993 Typically, evidence-based practice (EBP) refers to health practitioners applying the best currently available research evidence in the provision of health services. In other words, EBP challenges practitioners to “do things right” and to “do the right things”. EBP originated in medicine, where an estimated 10 000 new randomised controlled trials (RCTs) are published every year but where an estimated 20%–40% of services still do not reflect the best research evidence.1 Related disciplines such as psychology have also embraced the EBP movement to bridge research and practice in order to improve outcomes for people with mental disorders.2 In children’s mental health, high levels of unmet service need suggest a strong role for EBP. At any given time 14% of children experience mental disorders that cause significant distress and impair their functioning, yet only 25% of these children receive specialised mental health treatment services.3 It is also clear that children’s mental health services often fail to reflect the best available research evidence, leading researchers to argue that EBP is an ethical imperative if we are to improve children’s mental health.4,5 Despite being widely advocated, EBP has nevertheless proved difficult to implement. To some extent implementation barriers are a result of a restricted focus on interventions designed to change simple behaviours performed by individual practitioners, such as prescribing by physicians. These interventions have had only modest effects and need to be integrated with larger organisational and system changes that support EBP.1 However, a greater challenge may be posed by controversies about EBP’s narrow definitions of “evidence,” particularly when applied in mental health.6 Here, we discuss the controversies with regard to implementing EBP in children’s mental health. We illustrate the issues based …
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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 | 0.473 | 0.615 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.019 | 0.011 |
| Science and technology studies | 0.009 | 0.060 |
| Scholarly communication | 0.036 | 0.052 |
| Open science | 0.020 | 0.036 |
| Research integrity | 0.042 | 0.094 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".