Prediction of suicidal thoughts based on alexithymia and childhood traumas of divorced women
Bibliographic record
Abstract
Purpose: Women will experience new negative or positive emotions from the day of divorce. These feelings are so fluctuating that sometimes they are confused and bewildered by the oppositeness of these feelings. Therefore, the aim of this research was to predict suicidal thoughts based on childhood traumas and alexithymia of divorced women. Method: The current research was applied in terms of purpose and descriptive and correlational in terms of method. The statistical population of the current study included all divorced women who referred to counseling centers and psychology clinics in the 5th district of Tehran. Therefore, the sample size of the study was determined to be 250 people based on Klein's point of view. For the purpose of sampling, the statistical sample of the study was selected by using a targeted and available sampling method by referring or sending a questionnaire to counseling centers and psychological clinics in the 5th district of Tehran. The tools used for data collection included Childhood Trauma Questionnaire (CTQ), Beck Suicidal Ideation Questionnaire (BSSI) and Toronto Alexithymia Scale (TAS-20). In this research, the collected data were statistically analyzed using SPSS software, Pearson's correlation coefficient and multivariate regression analysis. Findings: The results of regression analysis showed that independent variables (childhood traumas and alexithymia) have 12% ability to predict the tendency to emotional divorce. Also, among independent variables, alexithymia with a beta coefficient of 0.16 and childhood traumas with a beta coefficient of 0.19 can positively predict subjects' suicidal thoughts (p<0.01). Conclusion: Therefore, it can be concluded that the suicidal thoughts of divorced women can be predicted through the variables of childhood traumas and alexithymia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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) | 0.002 | 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".