Family caregiver learning—how family caregivers learn to provide care at the end of life: A qualitative secondary analysis of four datasets
Bibliographic record
Abstract
BACKGROUND: Family caregivers are assuming growing responsibilities in providing care to dying family members. Supporting them is fundamental to ensure quality end-of-life care and to buffer potentially negative outcomes, although family caregivers frequently acknowledge a deficiency of information, knowledge, and skills necessary to assume the tasks involved in this care. AIM: The aim of this inquiry was to explore how family caregivers describe learning to provide care to palliative patients. DESIGN: Secondary analysis of data from four qualitative studies (n = 156) with family caregivers of dying people. DATA SOURCES: Data included qualitative interviews with 156 family caregivers of dying people. RESULTS: Family caregivers learn through the following processes: trial and error, actively seeking needed information and guidance, applying knowledge and skills from previous experience, and reflecting on their current experiences. Caregivers generally preferred and appreciated a supported or guided learning process that involved being shown or told by others, usually learning reactively after a crisis. CONCLUSIONS: Findings inform areas for future research to identify effective, individualized programs and interventions to support positive learning experiences for family caregivers of dying people.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".