Predicting of borderline personality disorder (BPD) based on emotional intelligence, apathy and empathy among the soldiers admitted to a military hospital
Bibliographic record
Abstract
"Due to the prevalence of BPD among soldiers and the importance of their mental health, the purpose of the study was to examine the relationship between emotional intelligence, alexithymia and empathy with BPD among the soldiers and to evaluate whether these variables could predict PBD. The study was cross-sectional with descriptive design. In this study, 150 soldiers with BPD admitted to 505 Army Psychiatric Hospital, Tehran were selected by convenience sampling and answered the following questionnaires: Bar-On Emotional Quotient Inventory (EQ-I), Toronto Alexithymia Scale (TAS-20) and Mehrabian and Epstein Empathy Questionnaire (EQ) with data analysis done in SPSS. The aspects of EQ had a reverse and significant relationship with BPD (r=-0.81), (p=0.01) and the relationship between alexithymia and PBD was direct and significant (p = 0.46) r), (p = 0.01), and the aspects of empathy and BPD were related inversely and significantly (r = -0.26), (p = 0.01). The results showed that the symptoms of BPD could be predicted somehow based on EI, alexithymia and empathy in soldiers with this disorder. From among the aspects of the variables studied, ability to solve problem, self-respect, self-actualization and optimism, objective thinking and difficulty in emotion recognition, emotional susceptibility, reactive empathy, participatory empathy and empathy toward others had the greatest roles in the prediction of BPD, but the subscale of independence with BPD examined among the soldiers was insignificant. These results are predictable in the context of emotional maladaptation, emotional distress, and mental impairment in BPD."
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".