Predicting the General Self-Efficacy of the People with Diabetes in Bandargaz- 2023: The Role of Rumination and Alexithymia
Bibliographic record
Abstract
Objective: This study aimed to investigate the impact of rumination and alexithymia on the general self- efficacy of individuals with diabetes. Materials and Methods: This correlational study targeted diabetic individuals aged 30–50 years residing in Bandargaz in 2023. A total of 217 participants were selected through convenience sampling. Data collection instruments included the General Self-Efficacy Scale (GSES), the Ruminative Response Scale (RRS), and the Toronto Alexithymia Scale (TAS). Data were analyzed using SPSS version 24, employing Pearson's correlation coefficient and stepwise regression analysis. Results: The results revealed a significant negative relationship between alexithymia and general self-efficacy. In the first step of the regression analysis, the beta coefficient was -0.446, indicating that a one standard deviation increase in alexithymia was associated with a 0.446 standard deviation decrease in general self- efficacy. In the second step, the beta coefficient for rumination was -0.152, suggesting that a one standard deviation increase in rumination was associated with a 0.152 standard deviation decrease in general self- efficacy. Conclusion: The findings demonstrate that higher levels of alexithymia and rumination negatively affect general self-efficacy in individuals with diabetes. Educational interventions and workshops focused on improving emotional regulation and cognitive coping strategies could enhance self-efficacy, enabling individuals to achieve personal goals and improve their overall well-being.
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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".