End-of-life healthcare decision-making: The intermediary role of the ethicist in supporting family caregivers and health professionals
Bibliographic record
Abstract
OBJECTIVE: Family support in end-of-life decision-making is critical, yet this issue receives little attention. The purpose of this research is to provide insight into how the clinical ethicist can effectively support family caregivers when making end-of-life healthcare decisions. It further suggests how the clinical ethicist can provide support to other healthcare professionals who work with family caregivers in making end-of-life healthcare decisions. PARTICIPANTS AND METHODS: Using a grounded theory approach, a theoretical sampling of 20 family caregivers previously involved in end-of-life decision-making were interviewed. Semi-structured in-depth interviews were audiotaped, and transcribed. RESULTS: Interviews were analyzed using a constant comparative approach. The analysis resulted in the identification of three categories related to the experience of decision-making: the impact of healthcare decision-making on individual and family well-being, the effectiveness of healthcare professionals in supporting families, and the role of the ethicist. CONCLUSIONS: The results demonstrated the challenges that family members encounter when faced with end-of-life decision-making. The participants identified the importance of having access to a clinical ethicist who, with objectivity and specific training, can better meet the needs of family caregivers and improve the process of decision-making at the end-of-life.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".