Caregiver Distress in Home Palliative Care
Bibliographic record
Abstract
Aim: This study aims to determine the symptom burden of caregivers who were following their loved ones at home and factors associated with this burden. Methods: From a consecutive number of patients followed at home by a specialistic palliative care team, a sample of 46 couples of patients-caregivers was screened. Epidemiological data of both patients and caregivers were collected, also including some variables, such as the level of religiousness, education, economic conditions, and financial distress. The Edmonton Symptom Assessment System (ESAS) was measured in both patients and caregivers. Caregivers were asked to provide a comment in a semi-structured interview, about “what do you think of your loved one’s suffering?” They were also invited to release any further comment. Results: Caregivers’ symptom burden was relevant. Sleep disturbances were even more relevant in caregivers. Caregivers with a lower level of education and financial distress experienced more global symptom burden. Caregivers manifested a deep sense of injustice and gripes regarding previous hospitalizations. Conclusion: There is an association between patient-reported severity of symptoms and caregiver symptoms. These data suggest delivering support to those caregivers who express higher levels of symptoms. There is a need for further research to explore the possible interventions to mitigate caregivers’ symptom burden.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".