Pediatric oncology caregiving as narrative repair: Restor(y)ing disrupted family biographies and damaged moral identities
Bibliographic record
Abstract
Drawing on Arthur Frank's conceptualization of narrative repair, we consider how pediatric oncology nurses restore and re-story the narratives of patients and families whose biographies have been thrown off course by the diagnosis and death of a child from cancer, as well as their own narratives as caregivers. Frank argued that when one's life story is shipwrecked by chronic or life-threatening illness, storytelling is way to reorient one's biography to a new ending, repairing the narrative wreckage created by the illness experience. In this critical narrative study with nine pediatric oncology nurses in Ontario, Canada, we highlight how, through physical, narrative, and moral proximity, nurses become entwined in their patients' and families' illness narratives, and how developing this narrative knowledge provides nurses with opportunities to steer families onto new terrain. As well, we examine how nurses re-story and repair their own identities as "good" caregivers in situations when they are prevented from acting on behalf of their pediatric cancer patients. These findings contribute to literature on illness narratives by considering narrative repair as a relational process enacted as part of pediatric oncology caregiving.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.021 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".