Flying Blind: Sources of Distress for Family Caregivers of Palliative Cancer Patients Managing Pain at Home
Bibliographic record
Abstract
Pain requiring treatment is experienced by many cancer patients at the end of life. Family caregivers are often directly implicated in pain management. This article highlights areas of psychosocial concern for family caregivers managing a family member's cancer pain at home as they engage in pain management processes. This article is based on the secondary analysis, guided by interpretive description, of data collected for a grounded theory study that explored the processes used by family caregivers to manage cancer patients' pain in the home. Interviews and field notes from 24 family caregiver interviews were examined to identify areas of family caregiver psychosocial distress. The analysis revealed that family caregivers experienced distress at different phases of the pain management process. Sources of distress for caregivers included feeling as though they were "in a prison" (overwhelmingly responsible), "lambs to slaughter" (unsupported), and "flying blind" (unprepared). In addition, family caregivers expressed distress when witnessing their loved one in pain and when pain crises invoked thoughts of death. In sum, family caregivers managing a loved one's cancer pain at home are at risk for psychosocial distress. This study identified four key sources of distress that can help health care professionals better understand the experiences of these family caregivers and tailor supportive interventions to meet their needs. Knowledge about sources of distress can help healthcare professionals understand the experiences of these family caregivers and tailor supportive interventions to meet their needs.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".