Bibliographic record
Abstract
Suffering is embodied, deeply personal, and yet connects people across time and space; that is, suffering can be socially relational. Relational suffering is potentially no more evident than in the context of children awaiting organ and bone marrow transplants. As the child with a life-threatening condition suffers, so do many others around them. In addition, a spate of professionals devote their lives to alleviating patient and family suffering. Currently, the social aspects of such suffering continue to be poorly understood by health researchers attending to organ and bone marrow transplantation processes. Specifically, evidence from the extant literature on organ and bone marrow transplantation hints to the waiting process as an important form of, patient and family, relational suffering. Yet very little is known about: how children and families wait; what happens physically, socially, emotionally, psychologically, spiritually, and existentially to children and their families while waiting; and, what resources and supports children and their families could use to relationally cope while/with waiting. Utilizing a narrative ethnographic approach (Gubrium & Holstein, 2009), I conducted a 17 month in-depth, field-based exploration through in situ interactions with four child organ and bone marrow transplant recipients, their families, and 20 HCPs both working with families or with experience in transplantations. I developed a theoretical, conceptual, and practical understanding of waiting/suffering by exploring families’ stories about waiting in uncertainty for a transplant over time. I explored what happened in and to relationships within families, between parents and friends, other parents of children with a life-threatening condition, and work colleagues. From the findings, I propose a working 'narrative roadmap' of children's and families’ experiences of waiting for a transplant to help guide families through the process of waiting, and to support HCPs in caring for them. Further, I propose a conceptual 'Relational Web of Suffering' as a heuristic tool for HCPs and bioethicists caring for families to illustrate how families’ social support networks operate while suffering, and people’s multi-dimensional (e.g., social, emotional, physical, spiritual) roles during waiting.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.014 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".