Poetics of brittle bone disease: using found poetry to explore childhood bioethics
Bibliographic record
Abstract
The gold standard for medical decision-making in pediatrics involves determining the “best interests” of the child and making the decisions accordingly. Accurately assessing the ethical concerns of children can assist care providers, such as parents, clinicians, and other healthcare professionals, in making care and discharge planning relevant to and reflective of what children need to flourish. However, the process of understanding children’s ethical concerns requires care providers to elicit their voices and address their hidden needs and desires into clinical care plans: a practice not commonly operationalized in hospitals. Found poetry was used to consolidate a three-year focused ethnography conducted at a large North American pediatric orthopedic hospital by rearranging interview transcripts into the poetic form. The ethnography demonstrated that children with Osteogenesis Imperfecta (OI or brittle bone disease) have developed complex strategies to navigate medical decision making processes and their communities despite prevailing societal notions of children’s fragility. The poems crystallize children’s rich and nuanced ethical concerns as well as the factors that support or thwart their moral agency within the hospital’s socioecological context. Found poetry thus can allow healthcare practitioners greater ethical insight into children’s needs and facilitate professional reflexivity.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.022 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 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".