Investigation of Relationship Between Social Capital and Quality of Life in Multiple Sclerosis Patients
Bibliographic record
Abstract
BACKGROUND: A large portion of existing medical research on Multiple Sclerosis patients focuses more on predicting medical variables (such as diagnosis, treatment) and individual variables such as the onset of disease, gender, etc., rather than broader socio-contextual factors. So that, here has yet been no study investigating factors such as social capital in Multiple Sclerosis patients. AIM: The purpose of this study is determining the relation between social capital and quality of life in Multiple Sclerosis patients who referred to Iran Multiple Sclerosis Society in 2012. METHOD: This cross-sectional study was conducted on 172 patients visiting Iran Multiple Sclerosis Society (Tehran) during 10 months via convenience samplings and face to face interviews. Tools for collecting data included World Bank's social capital integrated questionnaire (SC-IQ) and Multiple Sclerosis Quality of Life (MSQOL) -54. RESULTS: The average age of patients was 34/8 ± 9/6. The analysis of the six dimensions of social capital questionnaire showed that the highest average score belonged to membership in groups and networks (63/3 ± 15/3) and the lowest one was about trust and solidarity (44/3 ± 13/7).The results of the regression model showed that there is a statistical significant and positive relation between social capital and quality of life (P > 0.0001). CONCLUSION: Since the present study has been conducted for the first time in this vulnerable subpopulation of patients, its results can provide invaluable information regarding the quality of life and at the same time present hypotheses about the contributing factors.
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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 | 0.004 |
| 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 | 0.000 |
| 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".