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Record W6945191879 · doi:10.25384/sage.c.6087596.v1

The moral experiences of children with osteogenesis imperfecta

2022· other· en· W6945191879 on OpenAlexaboutno aff

Bibliographic record

VenueSage Journals Data · 2022
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsOsteogenesis imperfectaHealth careEthnographyParticipant observationQualitative researchResearch ethicsMEDLINEInclusion (mineral)

Abstract

fetched live from OpenAlex

BackgroundSerious ethical problems have been anecdotally identified in the care of children with osteogenesis imperfecta (OI), which may negatively impact their <i>moral experiences</i>, defined as their sense of fulfillment towards personal values and beliefs.Research aimsTo explore children’s actual and desired participation in discussions, decisions, and actions in an OI hospital setting and their community using art-making to facilitate their self-expression.Research designA focused ethnography was conducted using the moral experiences framework with data from key informant interviews; participant observations, semi-structured interviews, and practice-based research (art-making) with 10 children with OI; and local documents.Participants and research contextThe study was conducted at a pediatric, orthopedic hospital.Ethical considerationsThis study was approved by McGill University Institutional Review Board.Findings/resultsChildren expressed desires to participate in their care, but sometimes lacked the necessary resources and encouragement from healthcare providers. Art-making facilitated children’s voice and participation in health-related discussions.ConclusionsHealthcare providers are recommended to consider the benefits of art-making and educational resources to reduce discrepancies between children’s actual and desired participation in care and promote positive moral experiences.

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 categoriesMeta-epidemiology (narrow), Open science, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.525
Threshold uncertainty score1.000

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 science0.0060.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.1270.000

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 head0.036
GPT teacher head0.298
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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations0
Published2022
Admission routes1
Has abstractyes

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