Abstract WP298: Patient and Caregiver Physical Health During the First Year Post-stroke
Bibliographic record
Abstract
Background: Patients and their family caregivers collaborate to optimize patients’ recovery post-stroke. The literature suggests that stroke can negatively affect patient physical health and providing care can negatively affect caregiver wellbeing. We do not know how patient and caregiver health are linked. Therefore, the objective of this research was to examine the influence of patient, caregiver, and caregiving factors on patient and caregiver physical health. Method: Secondary analysis of a longitudinal cohort study of survivors of their first stroke and their family caregivers (n=399 dyads). Participants completed standardized surveys 1, 3, 6, and 12-months post stroke. Patient data included demographic information, Canadian Neurological Scale, stroke type, stroke location, and the Mini Mental Status Exam. Caregivers provided demographic information and completed the Caregiver Assistance Scale, Mastery Scale, Brain Impairment Behaviour Inventory-revised, and Centre for Epidemiological Studies Depression Scale. Patients and caregivers completed the SF-36 and the physical component score was used in the analyses. Hierarchical Linear Modelling was used to test full regression models using the longitudinal data. Results: Patients’ physical health was better when they were younger, had a milder stroke, fewer symptoms of depression, better cognitive health, and their caregiver had fewer symptoms of depression, provided less assistance, were female, and their spouse. Caregivers’ physical health was better when the stroke survivor had fewer memory problems, and caregivers had fewer symptoms of depression, were younger, and were male. Conclusions: Patient and caregiver factors were associated with each other’s physical health during the first year post-stroke. Caregiver depression symptoms were associated with poor physical health in both the patient and the caregiver. This suggests programs to address caregivers experience of depression may benefit both patients and caregivers physical 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".