Is physical activity related to a reduction in the severity of borderline personality disorder through less severe insomnia disorder?
Bibliographic record
Abstract
Introduction Borderline personality disorder (BPD) is associated with severe suffering and insomnia disorder (ID) (Fertuck et al., 2016; Galbiati et al., 2020). Objectives The aim was to investigate the negative association between self-reported physical activity (PA) and the severity of BPD with ID acting as a mediator (St-Amour et al., 2021). Methods The role of ID within the association of PA with BPD was tested using mediation analysis with the statistical program R 4.3 (N = 120; RStudio Team, 2020). Results Table 1 Mediation analysis results β se t p LLCI ULCI Effect a 0.07 0.05 1.46 0.15 -0.03 0.17 Effect b 0.41 0.09 4.60 < 0.001 0.23 0.59 Effect c 0.11 0.05 2.16 0.03 0.01 0.21 Effect c’ 0.08 0.05 1.70 0.09 -0.01 0.17 Note: β = beta coefficients; se = standard error; t = t-value; p = p-value; LLCI = lower limit confidence interval; ULCI = upper limit confidence interval. Effect c’: The association within the mediation analysis is not significant (β = 0.08, se = 0.05, p = 0.09). Effect a: PA is not significantly associated with ID (β = 0.07, se = 0.05, p = 0.15). Effect b and c: ID (β = 0.41, se = 0.09, p < 0.001) and PA (β = 0.11, se = 0.05, p = 0.03) are significantly associated with the severity of BPD. Image: Conclusions Accordingly, ID does not appear to affect the association of PA and BPD severity whereas fewer PA and severe ID can nonetheless have a positive association with the symptoms of BPD in independent ways. Disclosure of Interest None Declared
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".