Factors Influencing Family Caregivers' Ability to Cope With Providing End-of-Life Cancer Care at Home
Bibliographic record
Abstract
Dying at home is a goal promoted by many healthcare providers and governments as a way to enhance the dying experience for cancer patients and their family members. A key element to realizing this goal is the availability of a family member who is willing to provide care at home. Little research has been conducted on the factors that influence family caregivers' ability to cope with providing end-of-life cancer care at home. The purpose of this qualitative study was to describe factors influencing family caregivers' ability to cope with providing such care. An interpretive descriptive research design guided this study. Semistructured interviews with 29 active family caregivers were conducted and thematically analyzed. Our findings suggest 5 factors that influenced the caregivers' ability to cope: (1) the caregiver's approach to life, (2) the patient's illness experience, (3) the patient's recognition of the caregivers' contribution to his or her care, (4) the quality of the relationship between the caregiver and the dying person, and (5) the caregiver's sense of security. Findings provide important information to assist in informing health services and policies directed at enhancing family caregivers' coping abilities.
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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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 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".