Exploring COVID‐19 experiences for persons with multiple sclerosis and carers: An Australian qualitative study
Bibliographic record
Abstract
OBJECTIVE: The COVID-19 pandemic continues to impact communities around the world. In this study, we explored the COVID-19 experiences of persons with multiple sclerosis (MS) and carers. METHODS: Using a qualitative approach, interviews were undertaken with 27 participants residing in Australia (10 persons with MS, 10 carers and 7 MS service providers). Demographic and background data were also collected. Interviews were analysed using an inductive iterative thematic analysis. RESULTS: Across all groups, participants consistently recognized pandemic challenges and impacts for persons with MS and carers, especially due to disruption to routines and services. Emotional and mental health impacts were also highlighted, as anxiety, fear of contracting COVID-19 and stress, including relationship stress between persons with MS and carers and family members. Some persons with MS also mentioned physical health impacts, while for carers, the challenge of disruptions included increased demands and reduced resources. In addition to acknowledging challenges, persons with MS and carers also gave examples of resilience. This included coping and adapting by finding new routines and creating space through rest and breaks and through appreciating positives including the benefits of access to telehealth. CONCLUSION: Additional support is required for persons with MS and carers in navigating the impacts of COVID-19 as the pandemic progresses. In addition to addressing challenges and disruptions, such support should also acknowledge and support the resilience of people with MS and carers and enhance resilience through supporting strategies for coping and adaptation. PATIENT AND PUBLIC CONTRIBUTION: Service user stakeholders were consulted at the beginning and end of the study. They provided feedback on interview questions and participant engagement, as well as service user perspectives on the themes identified in the current study. Participants were provided with summaries of key themes identified and invited to provide comments.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".