The Lived Experience of Multiple Sclerosis Relapse: How Adults with Multiple Sclerosis Processed Their Relapse Experience and Evaluated Their Need for Postrelapse Care
Bibliographic record
Abstract
Background. Multiple sclerosis (MS) relapses can take a toll on individuals' health and quality of life. Given such consequences of relapses, postrelapse care beyond pharmacological approaches may play an important role in recovery. Nevertheless, how individuals with MS process their relapse experience and manage the consequences is still uncertain. Purpose. We conducted a qualitative study to understand relapse experiences and postrelapse care need from perspectives of adults with MS and identify relapse management patterns. Methods. We interviewed 17 adults with MS. Results. By examining combinations of three categories related to relapse experience, we identified four relapse management patterns: (i) Active Relapse Manager, (ii) Early-Stage Proactive Relapse Monitor, (iii) Adapted Passive Relapse Manager, and (iv) Passive Relapse Monitor. The relapse management patterns appear to associate strongly with the appraisal of the experience. Conclusions. The results of this study suggest the importance of understanding each patient beyond their functional limitations and the potential need for multidisciplinary postrelapse care which goes past restoring functional limitations at the acute phase. Future research to further understand the relapse management process at all stages of the healthcare continuum is a crucial step toward developing strategies to advance the current postrelapse care and to facilitate optimal recovery.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".