The ethical imagination of children with osteogenesis imperfecta: What children’s stories teach us about making safe space in hospital settings
Bibliographic record
Abstract
Puppetry, as a mode of research and therapeutic play, can render visible the silenced elements in children’s traumatic experiences of hospitalization and help articulate and facilitate their emotional safety and inclusion in perioperative processes. Research has long suggested that children are significantly more agential, morally sophisticated, and capable of understanding complex medical information than commonly believed. Nonetheless, dis/abled children, subject to both pejorative Western childhood and ableist narratives, commonly arrive in perioperative settings under-resourced in the savoir faire of adult negotiation and material independence needed to balance disadvantaging power asymmetries. The gap between agential capacity, reflective policy, and perioperative practices can engender real emotional harm to children, increase physical pain, and lengthen the time needed for physical wound healing. This applied theatre study developed a therapeutic puppet play interview technique with osteogenesis imperfecta (OI) affected children to co-create a theatrical script, wherein a so-called “bad” hospital was transformed into a “good” one. The play effectively helped map ethical harms experienced by frequently hospitalized children and illuminated children’s definition of effective trauma informed care through theatre.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.031 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| 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".