Examining the Relationship Between Family Caregivers' Emotional States and Ability to Empathize with Patients with Multiple Sclerosis
Bibliographic record
Abstract
BACKGROUND: Multiple sclerosis (MS) is the most common nontraumatic cause of disability affecting young adults in Canada. Caregivers of patients with MS are highly psychologically burdened. Empathy and helping behaviors are hallmarks of quality care, but when they are challenged, suboptimal patient care can result. We aimed to evaluate the prevalence of negative emotional states among primary caregivers of people with MS; the association between the caregiver's empathy-related behavior and the physical and cognitive impairment of the person with MS; and the association between the caregiver's emotional status and his or her empathy-related behaviors. METHODS: We conducted a descriptive, cross-sectional pilot study with family caregivers of noninstitutionalized individuals living with MS. We used univariate linear regression models for each potential predictor. The Kruskal-Wallis test was conducted to compare differences in caregiver empathic responses depending on Profile of Mood States subscale scores. RESULTS: Thirty percent of caregivers had elevated or very elevated mood scores, and such elevated scores were associated with greater functional impact of MS on the person with MS. Patient severity of cognitive impairment was not associated with caregiver mood scores. Caregiver mood state was not associated with empathy-related behaviors. Empathy-related behaviors were less frequent when levels of anger and hostility were higher, but this association did not reach statistical significance. CONCLUSIONS: Given the elevated levels of fatigue, depression, and anger observed among caregivers in this study, clinicians need to be aware of the potential impact of caregiving and to assess the needs of caregivers.
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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 | 0.002 | 0.008 |
| 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 | 0.001 |
| 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".