Editorial: Personality and Disease: New Directions in Modern Research
Bibliographic record
Abstract
The present Editorial of the Special Issue titled "Personality and Disease: New Directions in Modern Research" comprises six highly multidisciplinary papers, covering different clinical and research areas on the associations between personality and diseases. Notably, three contributions (Cavicchioli et al., 2021; Durosini et al., 2021; Galli et al., 2021) have provided a theoretical overview of current knowledge derived from the scientific literature on the role of personality traits or syndromes in developing and maintaining various medical conditions (especially, oncological diseases, cardiovascular pathologies, overweight and obesity, chronic pain presentations). All the other contributions (Buratta et al., 2021; Lanzara et al., 2020; Mokhtar et al., 2020) have described and addressed the findings of empirical investigations on this specific Research Topic, discussing clinically meaningful implications and suggesting best practices for achieving successful intervention outcomes.
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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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.017 | 0.014 |
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".