مقایسه هوش هیجانی، عزتنفس و ناگویی خلقی در زنان با افسردگی اساسی و زنان عادی در شهرستان شاهرود
Bibliographic record
Abstract
Introduction : Mood disorders, including major depression (MD), are among the most frequent psychological problems. Emotional intelligence which is an important component of mental health plays an important role in one's health; and also, by enhancing self-esteem, it is considered as a major source for support by which people can deal with negative events of every day life. The alexithymia Barrie might prevent a one’s emotion to be regulated and adjusted. The present study was aimed to assess emotional intelligence, self esteem and Alexithymia among women with majer depression disorder and and then compare them with normal women. Methods : The present work is a causal comparative study. We studied 60 women, including (30 women with majer depression and another 30 ones with normal conditions.), these participants were selected based on a conventional method from Rasol-Akram and nor- medical centers in spring and summer of 2014 (1393) in Shahrood, Iran. In order to analyze the data, we applied the following measuring tools: Sherink's Emotional Intelligence questionnaire, Cooper Smith's, self-esteem Inventory, Toronto Alexithymia scale-20 (TAS-20) and Beck depression Inventory (BDI) and also several identical variances (MANOVA). Results : Based on the results, there are significant differences between depressed and normal women, specifically at the levels of emotional intelligence, self-esteem and Alexithymia (P<0.001). Conclusion : Considering our findings, we suggest the importance and significance of the relationship between emotional intelligence, self-esteem and Alexithymia be taken into account in explaining depression. The findings in this work could be used in prevention and treatment of the programs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.066 | 0.022 |
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".