Relationship between Social Support, Time Perspective and Suicide Ideations in Patients with Multiple Sclerosis
Bibliographic record
Abstract
Introduction: Patients with Multiple Sclerosis (MS) are at risk for Suicide Ideation (SI). The relationship between Social Support (SS) and Time Perspective (TP) with SI is important among patients with MS. This study was performed to determine the prevalence of SI and the correlation between SS and TP with SI in Iranian patients with MS in Nahavand and Malayer. Methods: Using a cross-sectional analytic research design, we selected 79 participants among patients with MS in Nahavand and Malayer, Iran. Beck Scale for Suicidal Ideation, Multidimensional Scale of Perceived Social Support and Zimbardo’s Time Perspective Inventory were used for collecting the data. Results: The obtained results indicated that 30.3% of the patients with MS suffered from SI. There was a negative correlation between SS (from family, friends and significant other), Past Positive (PP) and Future (F) orientations and a positive correlation between Past Negative (PN) orientation and SI; SS from significant other and PP negatively predicted the SI in patients with MS. Conclusion: Based on the obtained results, the relationship between SS, PN, PP, F and SI and the role of SS from significant other and PP in predicting the SI in Nahavand and Malayer patients with MS were confirmed. Thus, it is necessary to develop support systems and apply the TP-based treatments for patients with MS who are at risk for SI.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".