Is healthy neuroticism associated with chronic conditions? A coordinated integrative data analysis
Bibliographic record
Abstract
Early investigations of the neuroticism by conscientiousness interaction with regards to health have been promising, but to date, there have been no systematic investigations of this interaction that account for the various personality measurement instruments, varying populations, or aspects of health. The current study - the second of three - uses a coordinated analysis approach to test the impact of the neuroticism by conscientiousness interaction on the prevalence and incidence of chronic conditions. Using 15 pre-existing longitudinal studies (N > 49,375), we found that conscientiousness did not moderate the relationship between neuroticism and having hypertension (OR = 1.00,95%CI[0.98,1.02]), diabetes (OR = 1.02[0.99,1.04]), or heart disease (OR = 0.99[0.97,1.01]). Similarly, we found that conscientiousness did not moderate the prospective relationship between neuroticism and onset of hypertension (OR = 0.98,[0.95,1.01]), diabetes (OR = 0.99[0.94,1.05]), or heart disease (OR = 0.98[0.94,1.03]). Heterogeneity of effect sizes was largely nonsignificant, with one exception, indicating that the effects are consistent between datasets. Overall, we conclude that there is no evidence that healthy neuroticism, operationalized as the conscientiousness by neuroticism interaction, buffers against chronic conditions.
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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 | 0.072 | 0.142 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| 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".