Impact of patient suffering on caregiver well-being: The case of amyotrophic lateral sclerosis patients and their caregivers
Bibliographic record
Abstract
The impact of patient suffering on family caregivers is an understudied but important topic. This study of patients with amyotrophic lateral sclerosis (ALS) and their caregivers examined the associations of two components of patient suffering: patient physical symptoms, and mental distress, as well as the patient's support for the caregiver, with caregiver well-being. The sample consisted of 52 patients with ALS and their caregivers. Patients and caregivers each completed a structured survey assessing multiple domains including demographics, health, and well-being. Specifically, patients rated their own physical symptoms and mental distress. Caregivers rated their own daily affect, and the extent to which they perceived the patient as supportive. Caregivers also reported whether or not they had found any benefit in dealing with the patient's illness. Regression analyses yielded significant associations of patient distress with caregiver negative affect; patient support was associated with greater caregiver positive affect, and patient symptoms and support were associated with greater likelihood of caregiver benefit finding. There was a significant two-way interaction of patient symptoms by support, namely, benefit finding was not only more likely with greater physical suffering and patient support, but it was also the case that caregivers who perceived the care recipient as unsupportive could only find benefit when this person experienced intense physical suffering. Support interventions for ALS patients and their caregivers should devote particular attention to how caregivers may be affected by witnessing their loved one's sufferings, as well as identify and address challenges in support exchanges between caregivers and patients.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".