پیش بینی افسردگی و افکار خودکشی زنان دارای همسر معتاد بر اساس استرس ادراک شده و ناگویی خلقی
Bibliographic record
Abstract
The purpose of this study was to investigate the role of perceived stress and alexithymia in predicting depression and suicidal thoughts in Women with addicted spouses. The present study was a descriptive-correlational study. 124 The woman with addicted spouse was available sampling method. The data were collected using Toronto Alexithymia Scale (TAS-20), Perceived stress scale, Beck depression inventory-II)BDI-II( and Beck Scale for Suicidal Ideation (BSSI) analyzed by SPSS-20 software, Pearson correlation coefficient and stepwise regression analysis. The findings of the present study showed that perceived stress and alexithymia can predict depression and suicidal thoughts in Women with addicted spouses. Perceived stress and alexithymia are factors contributing to the depression and suicidal ideation of Women with addicted spouses who can be considered in prevention programs for these psychological and social disorders.The purpose of this study was to investigate the role of perceived stress and alexithymia in predicting depression and suicidal thoughts in Women with addicted spouses. The present study was a descriptive-correlational study. 124 The woman with addicted spouse was available sampling method. The data were collected using Toronto Alexithymia Scale (TAS-20), Perceived stress scale, Beck depression inventory-II)BDI-II( and Beck Scale for Suicidal Ideation (BSSI) analyzed by SPSS-20 software, Pearson correlation coefficient and stepwise regression analysis. The findings of the present study showed that perceived stress and alexithymia can predict depression and suicidal thoughts in Women with addicted spouses.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".