General disease factor: evidence of a unifying dimension across mental and physical illness in children and adolescents
Bibliographic record
Abstract
Background Understanding the relationship between mental and physical health conditions is crucial for developing comprehensive healthcare strategies. The putative existence of a general disease factor ( d-factor ) that underlies the vulnerability to both physical and mental conditions could have important implications for our approach to health assessment and treatment. Objective To investigate the presence and characteristics of a general d-factor in children and adolescents. Methods This Swedish registry-based cross-sectional study included children and adolescents born between 1996 and 2003 with follow-up until 2013. We extracted data on 25 mental and physical health conditions according to the ICD-10 system. To determine the optimal dimensional structure of these conditions, several competing measurement models were tested, including correlated factors, one factor, various bifactor specifications and bifactor exploratory structural equation modelling (ESEM). Findings The study cohort included 776 667 individuals (mean age 13.96 years, IQR=11.96–16.04; 51% male). The bifactor ESEM model, including a general d-factor and specific mental and physical health factors, provided the best fit to the data compared to alternative models (Comparative Fit Index=0.971, Tucker-Lewis Index=0.962, root mean square error of approximation=0.007 (0.007–0.007)). The d-factor accounted for substantial variance (ω h =0.582, explained common variance (ECV)=0.498), while specific mental (ω hs =0.377, ECV=0.373) and physical (ω hs =0.423; ECV=0.130) factors also indicated additional significant unique contributions. Conclusions This study provided evidence for a multidimensional structure of health in children and adolescents, characterised by a general d-factor underlying both mental and physical conditions, alongside distinct domain-specific factors. These findings have important implications for clinical practice, providing evidence that suggests the need for more integrated approaches to health assessment and treatment that consider the interconnectedness of mental and physical health.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".