Embedding mental health interventions in early childhood education systems for at-risk preschoolers: an evidence to policy realist review
Bibliographic record
Abstract
BACKGROUND: Current early childhood systems of care are not geared to respond to the complex needs of preschoolers at risk for mental health problems in a timely, coordinated, multidisciplinary, and comprehensive fashion. Evidence-informed policy represents an opportunity for implementing prevention, promotion, and early intervention at the population or at-risk level. Exposure to risk factors as well as the presence of clinical disorders can derail the developmental trajectories of preschoolers, and problems may persist if left untreated. One way to address these multiple research-to-policy gaps are systematic reviews sensitive to context and knowledge user needs, such as the realist review. The realist review is an iterative process between research teams and knowledge users to build mid-level program theories in order to understand which interventions work best for whom and under what context. METHODS/DESIGN: The realist review employs five 'iterative' steps: (1) clarify scope, (2) search for evidence, (3) appraise primary studies and extract data, (4) synthesize the evidence, and (5) disseminate, implement, and evaluate evidence, to answer two research questions: What interventions improve mental health outcomes for preschoolers at risk for socio-emotional difficulties and under what circumstances do they work? and what are the best models of care for integrating mental health interventions within pre-existing early childhood education (ECE) services for at-risk children? Knowledge users and researchers will work together through each stage of the review starting with refining the questions through to decisions regarding program theory building, data extraction, analysis, and design of a policy dissemination plan. The initial questions will guide preliminary literature reviews, but subsequent more focused searches will be informed by knowledge users familiar with local needs and further building of explanatory program theories. DISCUSSION: Policy makers want to know what works best for whom, but are faced with a wide and disparate intervention literature for at-risk children. Applying evidence-based standards is a good start, but the chain of implementation between research results and how to match interventions sensitive to local context are ongoing challenges. TRIAL REGISTRATION: Prospero registration number: CRD42014007301.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".