Examining Associations of Functional Deficits and Mood States With Empathic Responses of Stroke Family Caregivers
Bibliographic record
Abstract
BACKGROUND: Stroke is one of the most prevalent chronic illnesses worldwide. Family caregivers can make a significant contribution toward patients' recovery. However, the patient's functional deficits and the caregiver's mood states can impact the caregiver's motivation to engage in empathic responses toward patients. Clinicians need help in recognizing patients at risk for not receiving empathic responses from family caregivers who are pivotal in long-term help and emotional support. OBJECTIVE: The aim of this study was to examine possible associations of family caregiver perceptions of functional deficits of patients who are poststroke and caregiver mood states with their empathic responses toward patients who are poststroke. METHODOLOGY: As guided by Davis' organizational model on empathy, we conducted a correlational descriptive study of associations among patient functional deficits, caregiver mood states, and caregiver empathic responses toward patients who are poststroke. Participants were requested to complete four questionnaires. RESULTS: Caregiver fatigue was the only mood state significantly associated with their empathic responses toward patients who are poststroke. CONCLUSIONS: The results of the current study contribute to the current state of the literature on poststroke care at home by highlighting the impact of caregiver mood states, particularly caregiver fatigue, on their empathic responses toward patients who are poststroke. Recommendations for clinical practice and future studies were made based on this study's results.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".