Predictors of p factor scores in children with chronic physical illness
Bibliographic record
Abstract
BACKGROUND: The p factor represents the overall liability for the development of mental illness within individuals and we have previously validated a bi-factor model of the p factor in children with chronic physical illness. OBJECTIVE: In this next phase, we modelled predictors of the p factor in this sample of children. METHODS: Data come from the ongoing Multimorbidity in Children and Youth Across the Life-course study. Data from 263 children with a chronic physical illness aged 2-16 years and their parents were collected over 24 months. The parent-reported Emotional Behavioural Scales was used to develop a bi-factor model of the p factor. Subsequently, p factor scores were extracted from the model and standardized (Mean = 100, SD = 15). Analysis of variance compared p factor scores across different physical illnesses. Multiple regression was used to identify multilevel baseline predictors of p scores. RESULTS: There was no significant difference in p scores across categories of physical illness (F = 0.44, p = 0.849). Factors predictive of elevated p scores were older child age (B = 0.44), higher level of disability (B = 1.03), elevated parent psychopathology (B = 0.22) and stress (B = 0.21), and living in communities with older age and lower labor force participation (B = 1.66) and higher concentrations of racialized/newcomer populations (B = 2.05). Lower p scores were associated with being female (B = -3.85) and having immigrant parents (B = -5.43). CONCLUSION: Factors predicting psychopathology, measured using p scores, in children with physical illness are multilevel. Fixed characteristics can inform targeted screening efforts, whereas modifiable characteristics are opportunities for upstream intervention in the context of family-centered integrated physical-mental health services.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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".