Do Integrated Hub Models of Care Improve Mental Health Outcomes for Children Experiencing Adversity? A Systematic Review
Bibliographic record
Abstract
This review assesses the effectiveness of integrated primary health and social care hubs on mental health outcomes for children experiencing adversity and describes common integration dimensions of effective hubs. PubMed, OVID Medline and PyschINFO databases were systematically searched for relevant articles between 2006–2020 that met the inclusion criteria: (i) interventional studies, (ii) an integrated approach to mental health within a primary health care setting, (iii) validated measures of child mental health outcomes, and (iv) in English language. Of 5961 retrieved references, four studies involving children aged 0–12 years experiencing one or more adversities were included. Most children were male (mean: 60.5%), and Hispanic or African American (82.5%). Three studies with low-moderate risk of bias reported improvements in mental health outcomes for children experiencing adversity receiving integrated care. The only RCT in this review did not show significant improvements. The most common dimensions of effective integrated hubs based on the Rainbow Model of Integrated Care were clinical integration (including case management, patient-centred care, patient education, and continuity of care), professional integration, and organisational integration including co-location. These results suggest hubs incorporating effective integration dimensions could improve mental health outcomes for children experiencing adversity; however, further robust studies are required. Registered with Prospero: CRD42020206015.
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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 | 0.009 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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, 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".