Exploring the self-Management strategies in people with multiple sclerosis
Bibliographic record
Abstract
Background & Aim: People with multiple sclerosis (MS) face numerous physical and psychological problems and use multiple strategies to manage these problems to reduce its impact on their own lives. The aim of this study is to explore the self-management strategies in people with MS. Methods & Materials: This study is a qualitative research with content analysis approach. Data were collected through semi-structured interviews with seven people with MS recruited from a rehabilitation clinic and the MS Support Community. Participants were selected through purposive sampling method. Data collection continued until data saturation. Trustworthiness criteria were considered to ensure the quality of findings. Data were analyzed using qualitative content analysis with conventional approach. Results: Analysis of the data ultimately led to the emergence of “attempt to maintain independence” as the main theme referring to the self-management strategies in people with MS. Selfmanagement strategies the participants used in this study were grouped into seven categories: disease acceptance, information enhancement, change of lifestyle, developing psycho-emotional balance, environmental modifications, improving financial credits, and promoting capabilities. Conclusion: People with MS use various self-management strategies for reducing their problems. Due to the nature of the disease, the use of self-management strategies can improve their control over illness. Understanding these needs and strategies helps health providers to provide better services to people with MS.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".