Exploring experiences of people with stroke and health professionals on post-stroke fatigue guidance: <i>getting the right people to the right care at the right time</i>
Bibliographic record
Abstract
Purpose This focus group study aimed to explore experiences and perceptions on post-stroke fatigue guidance in Dutch rehabilitation and follow-up care among people/patients with stroke and health professionals.Methods Ten persons with stroke and twelve health professionals with different professions within stroke rehabilitation or follow-up care in the Netherlands were purposively sampled and included. Eight online focus group interviews were conducted. We analysed the data using reflexive thematic analysis.Results Three themes were identified. Guidance in fatigue management did not always match the needs of people/patients with stroke. Professionals were positive about the provided fatigue guidance (e.g. advice on activity pacing), but found it could be better tailored to the situation of people/patients with stroke. Professionals believe the right time for post-stroke fatigue guidance is when people/patients with stroke are motivated to change physical activity behaviour to manage fatigue – mostly several months after stroke – while people/patients with stroke preferred information on post-stroke fatigue well before discharge. Follow-up care and suggestions for improvement described that follow-up support after rehabilitation by a stroke coach is not implemented nationwide, while people/patients with stroke and professionals expressed a need for it.Conclusions The study findings will help guide improvement of fatigue guidance in stroke rehabilitation programmes and stroke follow-up care aiming to improve physical activity, functioning, participation, and health.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 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".