Striving to Respond to Palliative Care Patients' Pain at Home: A Puzzle for Family Caregivers
Bibliographic record
Abstract
PURPOSE/OBJECTIVES: to describe the types of pain patients in palliative care at home experience and how family caregivers assess them and intervene. RESEARCH APPROACH: qualitative using grounded theory. SETTING: family caregivers' homes. PARTICIPANTS: 24 family caregivers of patients with advanced cancer receiving palliative care at home. METHODOLOGIC APPROACH: semistructured interviews and field notes. Data analysis used Strauss and Corbin's recommendations for open, axial, and selective coding. MAIN RESEARCH VARIABLES: pain, pain management, family caregivers, palliative care, and home care. FINDINGS: caregivers assessed different types of pain and, therefore, were experimenting with different types of interventions. Not all family caregivers were able to distinguish between the different pains afflicting patients, and, consequently, were not selecting the most appropriate interventions. This often led to poorly managed pain and frustrated family caregivers. CONCLUSIONS: The accurate assessment of the types of pain the patient is experiencing, coupled with the most appropriate intervention for pain control, is critical for optimal pain relief as well as supporting the confidence and feelings of family caregivers who are undertaking the complex process of cancer pain management. INTERPRETATION: nurses involved with patients receiving palliative care and their family caregivers should be aware of all types of pain experienced by the patient and how caregivers are managing the pain. Nurses should be knowledgeable about different pain relief interventions to help family caregivers obtain accurate information, understand their options, and administer these interventions safely and effectively.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".