A cognitive occupation-based programme for people with multiple sclerosis: A new occupational therapy cognitive rehabilitation intervention
Bibliographic record
Abstract
INTRODUCTION: Cognitive difficulties have been reported to have the greatest effect on function and quality of life in people with multiple sclerosis, affecting 50-60% of people. To date, few interventions have been developed to treat cognitive issues in multiple sclerosis. Here we report on a Cognitive Occupation-Based programme (COB-MS) for people with Multiple Sclerosis an evidence-based intervention to address everyday problems encountered due to cognitive difficulties. The aim of this research was to explore the views of people with multiple sclerosis and occupational therapists on the programme and its potential implementation in practice. METHODS: Data were elicited from a purposive sample of 12 people from two stakeholder groups, people with multiple sclerosis (n = 5) and occupational therapists (n = 7), through focus groups and interviews. The programme and related materials were presented, and contributions recorded, transcribed and thematically analysed. RESULTS: Two main themes were identified from analysis of the data: response to the intervention and challenges to implementing the programme. Occupational therapists agreed that the COB-MS is client-centred. People with multiple sclerosis thought that it was a validating intervention. The overall format was viewed to be useful and feasible. CONCLUSION: The COB-MS for people with Multiple Sclerosis is the first known cognitive intervention using an occupation frame of reference to address difficulties faced among persons with multiple sclerosis and was found to be timely and relevant to the needs of the population.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".