Health care professionals' grief after the death of a child
Bibliographic record
Abstract
OBJECTIVES: To evaluate the intensity of grief experienced by health care professionals (HPs) after the death of a child, to explore factors associated with a memorable death (defined as an unforgettable child's death that has touched them in their career) and to identify the needs of HPs. METHODS: A cross-sectional study was performed to assess emotional reactions, coping strategies and perceived needs of paediatric HPs in a general hospital. RESULTS: One hundred one HPs (46 nurses, 22 paediatric physicians, 11 paediatric residents, 13 respiratory therapists and nine 'others') completed the questionnaire. The level of grief experienced by HPs after a memorable death was intense. Respiratory therapists showed the highest mean (± SD) intensity of grief after a memorable death versus other HPs, as measured by the Texas Revised Inventory of Grief (TRIG) (29±15 versus 16±14; P=0.002). Younger HPs (20 to 25 years of age) reported higher early grief intensity than older ones (older than 50 years of age) (22±16 versus 10±8; P=0.01). There was no significant association between the TRIG score and an HP being a parent, having received palliative care training or the length of his/her relationship with the child and family. Seventy per cent of HPs spoke with their colleagues after the death of a child and 48% with family and friends. Many participants (37%) believed that this social support helped them the most. CONCLUSION: Grief after a child's death is intense for HPs. This emotional intensity and difference between professions raises issues about the emotional support received following the death of a patient.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".