Clinical Practice Guidelines About Screening for Disruptive Behavior Problems at Well-Child Visits: A Rapid Review of the Literature on the Accuracy of Parents’ Behavioral Concerns
Bibliographic record
Abstract
The current recommendations of the Canadian Paediatric Society about monitoring children’s disruptive behaviors at well-child visits call for screening if behavioral concerns are being raised by parents. But do parents’ concerns about their child’s behavior constitute a reliable means for primary care providers (PCPs) to decide either in favor or against screening? We conducted a rapid systematic review of the literature by identifying documents that cited the landmark study by Glascoe and her colleagues (1991) on the accuracy of behavioral concerns at identifying children with a disruptive behavior problem. Citation tracking was done using Web of Science (Core Collection; 17 October 2018) and SCOPUS (19 October 2018). Only one recent published study was identified. The calibration of concerns’ specificity (and other indices alike) yielded, at best, a fair value of the weighted kappa coefficient κ(0,0) (i.e., 0.255 and 0.094). Also, the calibration of concerns’ sensitivity (and other indices alike) yielded, at best, a moderate value of the weighted kappa coefficient κ(1,0) (i.e., 0.533 and 0.392). Overall, the results do not support the current recommendations. In fact, behavioral concerns do not provide PCPs with enough information to reach a decision about screening. We discuss different ways of gathering the necessary information.
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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 | 0.145 | 0.401 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.028 | 0.018 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.011 | 0.009 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".