Paramedics' experiences with death notification: a qualitative study
Bibliographic record
Abstract
Objective To explore paramedics’ experiences and coping strategies with death notification in the field. Methods Urban and urban/rural paramedics participated in four focus groups across Ontario, Canada.They were asked about their experiences communicating death notifications and the support they received. Transcripts were analysed using the constant comparative method. Themes were generated inductively. Results Twenty-eight paramedics participated. Four themes emerged: the practical aspects of deathnotification, how paramedics acknowledge the emotional toll, how they manage the emotional toll, and the support mechanisms they used. Communicating a death notification is stressful and paramedics’ personal attitudes to death influence how they communicate a notification. Switching roles from clinician to supporter is challenging. Deaths that are unexpected, traumatic, obvious, involve children, with which the paramedic identifies, or are the paramedic’s first experience are especiallystressful. Paramedics receive support by talking to peers and using informal support networks. They prefer support from people who have had similar experiences. Conclusion Paramedics’ experiences with death notification are stressful, challenging, andrewarding. More formal support for paramedics is necessary, especially when the nature of the death is distressing. Our study suggests that further training is required to increase paramedics’ comfort with this challenging communication.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".