Intra-Family End-Of-Life Conflict: Findings of a Research Investigation to Identify Its Incidence, Cause, and Impact
Bibliographic record
Abstract
With few investigations of intra-family end-of-life conflict, this study sought to identify its incidence, cause, and impacts. A questionnaire was completed by 102 hospice/palliative nurses, physicians, and other care providers in Alberta, a Canadian province. Participants reported on how often they had observed intra-family conflict when someone in the family was dying, and the impacts of that conflict. 12 survey participants were then interviewed about the intra-family conflict that they had encountered, with interviews focused on why conflict occurred and what the impacts (if any) were. Nearly 80% of families were thought to experience end-of-life conflict, periodically or continuously, among various family members. The interviews confirmed three reasons for intra-family end-of-life conflict and three conflict outcomes that were revealed in a recent literature review. The findings indicate routine assessments for intra-family end-of-life conflict are advisable. Attention should be paid to preventing or mitigating this conflict for the good of all.
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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".