Cognitive impairment and its impact on employment: A qualitative interview-based study involving healthcare professionals and people living with multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: Cognitive impairment can considerably impact the work life of people who have multiple sclerosis (MS). Cognitive symptoms are associated with an increased likelihood of unemployment, changes in employment and decreased working hours. This qualitative interview-based study aims to gather real-word experiences and perspectives from both people living with MS and healthcare professionals, to explore how cognitive symptoms are experienced in the workplace, how their impact is addressed, and what can be done to support people in managing and minimizing this impact on employment. METHODS: Semi-structured, one-to-one interviews were conducted with people living with MS who experience cognitive symptoms, and with healthcare professionals working in MS care. Participants were recruited from a healthcare market research agency based in the United States. The data obtained from the interviews were subsequently analysed using a Grounded Theory method, in order to identify the core themes that form the basis of this paper. RESULTS: A total of 20 participants (n = 10 people living with MS; n = 10 healthcare professionals) from the United States were interviewed. Overall, 9 themes were identified from the raw data, which were grouped into three core themes describing the perspectives and experiences reported by both people living with MS and healthcare professionals: (1) The implications of cognitive symptoms on work; (2) Challenges in addressing cognitive impairment and its impact on work in MS care; (3) Strategies and support for managing the impact of cognitive symptoms. CONCLUSION: The real-world insights of PwMS and HCPs gained from this qualitative study show that a multi-faceted approach to addressing cognitive impairment and its impact on the employment of PwMS is required. Workplace adjustments can range from self-implemented changes to changes put in place by employers to accommodate the various ways in which cognitive symptoms may impact a person's work. This study provides valuable information on how people living with MS can be affected by cognitive symptoms in the context of their employment; furthermore, that preparing early when possible and maintaining a proactive approach to managing their impacts on work are important for maintaining a good quality of life.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".