Bibliographic record
Abstract
BACKGROUND: The p factor represents the overall liability for the development of mental illness. While evidence supporting the p factor in adults has been reported, studies in children are fewer, and none have examined the p factor in children with chronic physical illness (CPI). OBJECTIVE: We aimed to model the p factor in a longitudinal sample of children with CPI using a parent-reported checklist and examine its construct validity against a structured diagnostic interview. METHODS: We used data from 263 children aged 2-16 years diagnosed with a CPI who were enrolled in the Multimorbidity in Children and Youth across the Life-course (MY LIFE) study. The p factor was modelled using the Emotional Behavioural Scales over 24 months using confirmatory factor analysis. Validation of the p factor was set against the Mini International Neuropsychiatric Interview for Children and Adolescents. RESULTS: = 9.66(4), p = 0.047]. p factor scores were correlated with the number of different mental illness diagnoses (r = 0.71) and total number of diagnoses (r = 0.72). Dose-response relationships were shown for the number of different diagnoses (p < 0.001) and total number of diagnoses (p < 0.001). CONCLUSION: In this first study of the p factor in children with CPI, we showed evidence of its bi-factor structure and associations with mental illness diagnoses. Mental comorbidity in children with CPI is pervasive and warrants transdiagnostic approaches to integrated pediatric care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".