Patients experience of cognitive fatigue post-stroke: an exploratory study
Bibliographic record
Abstract
PURPOSE: Cognitive fatigue is commonly reported and described as disabling by patients recovering from neurological conditions including stroke. However, cognitive fatigue is usually underdiagnosed among stroke survivors which leads to a lack of specific treatments for this condition. Therefore, the aim of this study was to explore post-stroke cognitive fatigue as it is experienced by stroke survivors. METHODS: This qualitative research followed the principles of descriptive phenomenology within a constructivist paradigm. Individual semi-structured interviews were conducted with stroke survivors experiencing post-stroke cognitive fatigue recruited through the Heart and Stroke Foundation, the Canadian Partnership for Stroke Recovery, and social media posts. Data were analyzed through inductive content analysis. RESULTS: Eleven stroke survivors participated. The analysis revealed five themes illustrating the experience and descriptions of post-stroke cognitive fatigue: (1) characteristics, (2) aggravating factors, (3) management, (4) effect of cognitive fatigue on daily life, and (5) social awareness and support. CONCLUSION: This study highlights the complexity of post-stroke cognitive fatigue. Cognitive fatigue becomes more evident after discharge; therefore, clinicians should consistently screen for it and provide proper education to the patients and their carers.IMPLICATIONS FOR REHABILITATIONCognitive fatigue is a complex phenomenon that can negatively affect the daily life of stroke survivors.Sensory-overloaded environments, emotional distress, poor sleep, and engaging in complex cognitive tasks can trigger post-stroke cognitive fatigue.More education on the concept of cognitive fatigue should be provided to healthcare providers to be able to identify and manage this symptom properly.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".