Grief reactions and impact of patient death on pediatric oncologists
Bibliographic record
Abstract
BACKGROUND: To examine pediatric oncologists' grief reactions to patient death, and the impact patient death has on their personal and professional lives. PROCEDURE: The grounded theory method was used. Data was collected between March 2012 and July 2012 at two academic centres in Canada. Twenty-one out of 34 eligible pediatric oncologists at different stages of their career were recruited and interviewed about their experiences with patient death. Inclusion criteria were: being able to speak English and having had a patient die in their care. The participants formed three groups of oncologists at different stages of career including: fellows, junior oncologists, and senior oncologists who varied in sub-specialties, gender, and ethnicities. RESULTS: Pediatric oncologists reported a range of reactions to patient death including sadness, crying, sleep loss, exhaustion, feeling physically ill, and a sense of personal loss. They also reported self-questioning, guilt, feelings of failure and helplessness. The impact of these deaths had personal consequences that ranged from irritability at home, feeling disconnected from family members and friends, and becoming more desensitized towards death, to gaining a greater and more appreciative perspective on life. Professional impacts included concern about turnover or burnout at work and improving holistic care as a result of patient deaths. CONCLUSIONS: Grief over patient death and the emotional labour involved in these losses are a robust part of the pediatric oncology workplace and have major impacts on pediatric oncologist's personal and professional lives. Interventions that focus on how to help pediatric oncologists deal with these reactions are needed.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".