Assessment of stroke survivors on an inpatient rehabilitation unit
Bibliographic record
Abstract
Background: Following a stroke, individuals often experience substantial difficulties, including speech and language problems, mobility, cognitive and emotional problems. Clinical guidelines for stroke care highlight the importance of assessing and monitoring mood and cognitive functioning of stroke survivors, in order to support the individual and guide multidisciplinary teams in holistic rehabilitation. This paper presents how psychological wellbeing assessments combine mood and cognitive screening and inform nursing and rehabilitation activities. Aim: Psychological wellbeing assessments were evaluated in view of their suitability for meeting the professional and stroke standards for mood and cognitive assessments. It was aimed to present how such assessments inform nursing care, individual rehabilitation programmes and discharge arrangements. Methods: A total of 83 patients were assessed with mood and cognitive screening procedures. As many as 75 patients had a diagnosis of ischaemic stroke and eight patients experienced an intracerebral haemorrhage. All patients were assessed on an inpatient stroke rehabilitation unit. More than half of the patients (43) were male, while 40 were female. The average age was 71 years with a range from 23–96 years. Results: Mood assessments were carried out with 76 patients. Among these, 14 patients presented borderline clinical mood symptoms and six patients showed significant psychological disturbances. More than three-quarters of patients (65) completed cognitive assessments. Outcomes informed the clinical team about their baseline functions, capacity issues and the data contributed to specific cognitive skills training and functional occupational therapy programmes. Conclusions: Psychological wellbeing assessments offer opportunities for nursing and multidisciplinary teams to cover a range of mood and cognitive issues. Combined mood and cognitive screenings are practical and effective procedures helping to meet service standards and requirements.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".