Validating existing clinical cut-points for the parent-reported Strengths and Difficulties Questionnaire in a large sample of Canadian children and youth
Bibliographic record
Abstract
INTRODUCTION: The Strengths and Difficulties Questionnaire (SDQ), for assessing behavioural and emotional difficulties, has been used internationally as a screening measure for mental health problems. Our objective was to validate the existing (British) SDQ cut-points in a sample of Canadian children and youth, and develop new Canadian SDQ cut-points if needed. METHODS: This study includes data from children and youth aged 6 to 17 years from the Canadian Health Measures Survey (n = 3435) and outpatient records from the Children's Hospital of Eastern Ontario (n = 1075). The parent-reported SDQ data were collected. We adjusted the existing SDQ cut-points using a distributional and receiver-operating characteristic (ROC) curve approach. We subsequently calculated the sensitivity, specificity and diagnostic odds ratio of the existing and new SDQ clinical cut-points to determine whether the new cut-points had better clinical utility, using both analytic approaches. RESULTS: Our data show differences in the screening effectiveness between the existing British and the Canadian-specific clinical cut-points. Specificity is maximized using the Canadian distributional cut-points, improving the likelihood of identifying true negative results. The total SDQ score met the threshold for clinical utility (diagnostic odds ratio > 20) using both the existing and new cut-points; however, the individual scales did not reach clinical utility threshold using either cut-points. CONCLUSIONS: Future Canadian SDQ research should consider the new cut-points derived from our study population and the existing British cut-points to allow for historical and international comparisons.
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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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".