Evaluating Screening Guidelines for Disruptive Behavior Problems in Children: A Systematic Review of the Accuracy of Parents’ Concerns
Bibliographic record
Abstract
Disruptive behavior problems (DBPs) in young children are early indicators of potential disruptive behavior disorders (DBD), which can lead to negative health and social outcomes. Secondary prevention strategies that target DBPs may facilitate early interventions and reduce these risks. Current Canadian pediatric practice guidelines provide an example one such strategy and suggest screening for DBPs only if a child’s parent reports concerns about their behavior. This systematic review sought to determine if parents’ concerns can provide enough information to justify a decision in favour of, or against, screening for DBPs. The protocol was registered on Prospero (CRD42021157492), and no funding was received. Six databases were searched (March 23–26, 2022) for prospective, retrospective, or naturalistic studies assessing the diagnostic accuracy of parents' concerns. Studies were included if they elicited parents' concerns about their child's behavior via an index test, used a reference standard to identify DBPs in children aged 0-5, and reported true/false positive and true/false negative outcomes. Studies were excluded if they did not include children in the target age range, did not report the outcomes of interest, used inappropriate sampling methods, measured heterogeneous mental health problems, elicited heterogeneous concerns from parents, or if they were not a primary analysis of data. Risk of Bias was assessed using the QUADAS-2 tool, and results were synthesized to produce calibrated estimates of the accuracy of parents’ concerns in the form of weighted kappa coefficients. Of 53 studies reviewed, only one met the eligibility criteria. Moderate agreement was found between the absence of DBPs and parents' concerns (k = 0.533, 95% CI: 0.501-0.564) and fair agreement for the presence of DBPs and parents’ concerns (k = 0.255, 95% CI: 0.238-0.272). These findings suggest that parents' concerns alone may not be sufficiently accurate to guide clinical screening decisions, highlighting a significant gap in the literature. Further research is needed to validate this approach. Until more data becomes available, clinicians should be cautious when interpreting the presence or absence of parents’ concerns about their child’s behavior, and in using parents’ concerns when making decisions to screen for DBPs.
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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 | 0.061 | 0.297 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.021 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.003 |
| 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".