Are physical activity and nutrition linked to personality disorders? Health habits and personality disorders: A scoping review
Bibliographic record
Abstract
Individuals with personality disorders (PDs) have a decreased life expectancy compared with the general population in part due to physical illnesses. Many hypotheses have been suggested to explain those physical illnesses such as hormone imbalance, medication, lack of physical activity, and unhealthy diet. However, little is known about the relation between lifestyle and PDs. The purpose of this scoping review is to regroup the available information on this topic. We searched the literature up to February 2021 using four databases and found 21 articles analyzing the relation between lifestyle and PDs in observational studies including 153,081 participants from diverse populations going from general population to adults in psychiatric care. Most studies used measures of lifestyle as control variables or did not use lifestyle variables at all. Moreover, the instruments used to measure lifestyle variables lacked precision at best. Two studies demonstrated a relation between early malnutrition and further development of PDs, but those results may be influenced by confounding variables and cannot indicate a clear link between nutrition and personality disorder. The lack of solid evidence we observed is surprising, considering the multiple benefits individuals with PDs could get from a healthy lifestyle. More studies are needed to thoroughly analyze the impact of lifestyle on PDs and vice versa.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".