When a child dies: pediatric oncologists' follow‐up practices with families after the death of their child
Bibliographic record
Abstract
OBJECTIVES: Follow-up practices with bereaved families are considered a part of good medical care, yet little is known about pediatric oncologists' protocol with families when their patients die. The objective of this study was to examine follow-up practices employed by pediatric oncologists after patient death using an in-depth qualitative analysis. METHODS: The Grounded Theory method of data collection and analysis was used. Twenty-one pediatric oncologists at two Canadian pediatric hospitals were interviewed about their follow-up practices with bereaved families after patients died. Line-by-line coding was used to establish codes and themes, and constant comparison was used to establish relationships among emerging codes and themes. RESULTS: Pediatric oncologists actively engage in follow-up practices that include making phone calls, sending an email or condolence card, attending funerals or visitations, having long-term and short-term meetings with parents, and attending hospital or departmental memorials for the deceased child. Attending funerals or visitations was less frequent and varied widely across pediatric oncologists. Reasons for not participating in bereavement follow-up practices included logistical, emotional, and practical considerations. CONCLUSIONS: While the majority of pediatric oncologists at two Canadian centers engage in some follow-up practices with bereaved families, these practices are complex and challenging because of the emotional nature of these interactions. Medical institutions should provide both structured time for this follow-up work with families, as well as medical education and financial and emotional support to pediatric oncologists who continue caring for these families long after their child has died.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".