What family caregivers learn when providing care at the end of life: A qualitative secondary analysis of multiple datasets
Bibliographic record
Abstract
OBJECTIVE: Although growing numbers of family members provide end-of-life care for dying persons, caregivers frequently report lacking essential information, knowledge, and skills. This analysis explicates what family members learn during the process of providing end-of-life care. METHOD: Four qualitative interview studies of family caregivers to those at the end of life (n = 156) formed the basis of a secondary data analysis. RESULTS: Thematic and cross-comparative analyses found three general kinds of learning that were described-knowledge about: (1) the situation and the illness (including what to expect), (2) how to provide care, and (3) how to access help. Learning gaps, preferences, and potential inequities were identified. Further, in some instances, participant talk about "learning" appears to reflect a meaning-making process that helps them accept their situation, as suggested by the phrase "I have had to learn." SIGNIFICANCE OF RESULTS: Findings can inform the development of individualized educational programs and interventions for family caregivers.
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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 | 0.029 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".