Changing expectations toward end-of-life communication: An experimental investigation
Bibliographic record
Abstract
To investigate the effect of a) a brief video intervention and b) end-of-life (EOL) conversations with relatives on EOL communication expectations. 272 participants from the general population were randomly assigned to three different video conditions (Intervention group: Persons reporting positive EOL conversation experiences +imagination task, Control group 1: Video unrelated to EOL topics, Control group 2: Persons reporting different attitudes toward EOL conversations +imagination task). Primary outcome was negative expectations. After the videos, participants were invited to have their own conversation with a loved one in the following two months. Data were collected before (pretest) and after watching the videos (posttest) as well as at a two-months follow-up. Between pre- and posttest, negative expectations decreased significantly more in the IG compared to CG1 ( b = 0.15, t = 2.08 , p = .020) and CG2 ( b = 0.21, t = 2.94, p = .002). Across conditions, participants having had a conversation between posttest and follow-up reported significantly stronger declines of negative expectations ( b = 0.35, t = 3.54, p < .001). In the short term, a brief video intervention can change expectations toward EOL communication. EOL conversations with relatives also have the potential to reduce negative expectations. Based on the findings, larger community-based interventions could be developed in order to increase EOL communication. • Evidence shows that end-of-life (EOL) communication is often avoided. • Negative expectations can be an important avoidance factor. • The study findings suggest that a brief video intervention can change expectations. • The results also suggest that conversations with relatives can change expectations. • Based on these findings, larger community-based interventions should be developed.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".