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Record W4281666594 · doi:10.1080/08893675.2022.2043120

Poetics of brittle bone disease: using found poetry to explore childhood bioethics

2022· article· en· W4281666594 on OpenAlexaff
Brenda L. Cleary, Franco A. Carnevale, Argerie Tsimicalis

Bibliographic record

VenueJournal of Poetry Therapy · 2022
Typearticle
Languageen
FieldMedicine
TopicEthics and Legal Issues in Pediatric Healthcare
Canadian institutionsShriners Hospitals for Children - CanadaMcGill University
Fundersnot available
KeywordsHealth careBioethicsContext (archaeology)EthnographyAgency (philosophy)ReflexivityPsychologyMedicineSociologyNursingSocial sciencePolitical scienceHistoryLaw

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.602
Threshold uncertainty score0.742

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.162
GPT teacher head0.424
Teacher spread0.262 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2022
Admission routes1
Has abstractyes

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