Identification of Preschool Children with Mental Health Problems in Primary Care: Systematic Review and Meta-analysis.
Bibliographic record
Abstract
OBJECTIVE: Primary care practitioners determine access to care for many preschool children with mental health (MH) problems. This study examined rates of mental health (MH) problem identification in preschoolers within primary healthcare settings, related service use, and MH status at follow-up. The findings may inform evidence-based policy and practice development for preschool MH. METHOD: were searched from inception to March 7, 2018 for reports in which a screening measure was used to identify MH problems in children aged 24-72 months, seen in primary and community health care settings. Meta-analyses, using random effects models to provide pooled estimates, were used when three or more studies examined identification rates. Findings on service use and persistence of disorders are summarized. RESULTS: Thirty-five publications representing 21 studies met the inclusion criteria. MH problems were identified in 17.6% of preschoolers (95% Confidence Interval (CI): 11.1-24.1), Q = 4.9, p > 0.1 by primary/community healthcare practitioners. Psychiatric diagnoses were identified in 18.4% of preschoolers (95% CI: 12.3 - 24.4), Q= 1.6, p > 0.1. Based on three studies, parents of 67-72% of identified children received advice and 26-42% received specialist referrals. In the subset of studies examining persistence of MH disorders, 25-67% of identified children had MH disorders after one to three years. CONCLUSION: While the identification rate by primary/community practitioners is similar to the diagnostic rate, these may not consistently be the same children. Substantial variability in management and outcomes indicate need for more rigorous evaluation of primary care services for this population.
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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 | 0.015 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.033 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".