Pediatric oncologists' coping strategies for dealing with patient death
Bibliographic record
Abstract
This research examined pediatric oncologists coping strategies when their patients died of cancer. Twenty-one pediatric oncologists at 2 Canadian pediatric academic hospitals were interviewed about their coping strategies when patients died or were in the process of dying. The grounded theory method of data collection and data analysis were used. Line-by-line coding was used to establish codes and themes and constant comparison was used to establish relations among emerging codes and themes. Pediatric oncologists used engagement coping strategies with primary and secondary responses including emotional regulation (social support and religion), problem solving (supporting families at end of life), cognitive restructuring (making a difference and research), and distraction (breaks, physical activity, hobbies and entertainment, spending time with own children). They also used disengagement coping strategies that included voluntary avoidance (compartmentalization and withdrawing from families at end of life). Given the chronic nature of patient death in pediatric oncology and the emotionally difficult nature of this work, medical institutions such as hospitals have a responsibility to assist pediatric oncologists in coping with this challenging aspect of their work. Future research is needed to evaluate how best to implement these changes on the institutional level to help oncologists cope with patient death and the effect of using these strategies on their quality of life.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".