Quality of Life Among Multiple Sclerosis Patients in Terms of Mental Health
Bibliographic record
Abstract
This study aimed to identify the mental health quality of life among patients with multiple sclerosis in Jordan. Thus, a descriptive quantitative design was used on a total of (N=100) Multiple Sclerosis patients that were randomly selected by using convenience sampling from the Health Insurance Center in the capital Amman, Jordan. Outcome measurement tools were the demographic data form and the Multiple Sclerosis Quality of Life-54 (MSQOL-54) Scale. The demographic data form consisted of questions about: age in years, gender, stage of multiple scleroses, and physical activities. The Multiple Sclerosis Quality of Life-54 (MSQOL-54) consisted of two domains the physical health composite and the mental health composite. In this study the mental health composite were used by the participants. The results revealed that the QOL- Mental Health Composite among patients with multiple sclerosis was 33.9 + 33.6. Moreover, there was no significant difference in score for male and females p=.874. In addition, there was no significant difference in QOL mental health scores for the age groups p=.165. Finally, there was a significant difference in scores for participants and non-participants in physical activity p=.000. Accordingly, this research concluded that Multiple sclerosis patients’ have a low quality of life in terms of mental health. In addition, practicing physical activities have a positive effect on the quality of mental health among multiple sclerosis patients.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".