Death notification education for paramedics: Past, present and future directions
Bibliographic record
Abstract
Objective: To explore paramedics' experiences with death notification education. Methods: Four focus group sessions attended by paramedics were mediated by a professional facilitator mn urban and urban/rural areas of Ontario, Canada. Paramedics were asked about their experiences with death notification education and what format and content of education they would like. Transcripts were analysed using the constant comparative method. Themes were generated inductively. Results: Twenty primary care paramedics and eight advanced care paramedics with mean experiences of 8.5 and 21.5 years respectively, participated. They reported minimal death notification education in their initial and professional education. They support education in college programmes, new paramedic orientation, and through mentoring. Paramedics learn to communicate death notifications by observing others and by trial and error. They want to learn about this topic through evidence-based continuing education (CE) sessions delivered by a trained facilitator or through online independent study. Experiential methods incorporating role-play and feedback are supported. A trained peer or health care professional with similar experiences would be best to teach paramedics about death notification. Paramedics want to learn about the practical aspects of communicating death notifications, managing the reactions of the bereaved, the cultural and religious aspects of death, as well as their personal reactions to death. Paramedics’ attitudes to death notification education are influenced by their work environment. Conclusions: There is a lack of formal death notification education for paramedics. Formal education should be implemented to reduce the stress of communicating death notifications for the paramedic and for the bereaved.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".