Stressful Life Events in Iranian Adults Society: Identification and Redefinition of Dimensions
Bibliographic record
Abstract
Background and purpose: Stressful life events can lead to psychological problems, heart disease, stroke, etc. Multidimensional nature of stress calls for advanced statistical methods that could evaluate these dimensions based on symptoms of stress. Therefore, current study aimed at identifying and redefining the dimensions of the stressful life events (SLE) questionnaire using a higher order factor model. Materials and methods: This cross-sectional study was done in 4763 people participating in a project called SEPAHAN in Isfahan, Iran 2010. The perceived stress level was evaluated by the Iranian version of SLE questionnaire. First and second order exploratory and confirmatory factor models were applied for data analysis using AMOS V20. Results: According to exploratory factor analysis, 11 domains of stress (first order factors) were extracted from 44 items, which explained 51.42% of the total variance. Based on these domains, two dimensions were identified as second-order factors (stressors), including individual and social stressors that explained 17.3% and 25.6% of total variance, respectively. The similar structure was identified both in total sample and in men and women separately. Also, the results in exploratory factor analysis were confirmed by confirmatory factor analysis (CFI =0.78, GFI =0.89, RMSEA =0.05). It was found that the second-order factor model well fitted to the dimensions of the questionnaire identified. Conclusion: SLE questionaire has11 domains and two higher dimensions (individual and social stressors). Studying the types of stressors could be used in preventive strategies against mental health problems and also in training useful copying styles.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".