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Record W4297461335 · doi:10.1177/13674935221112156

Moral experiences of children with medical complexity: A participatory hermeneutic ethnography in Brazil

2022· article· en· W4297461335 on OpenAlexafffund
Raíssa Passos dos Santos, Mary Ellen Macdonald, Franco A. Carnevale

Bibliographic record

VenueJournal of Child Health Care · 2022
Typearticle
Languageen
FieldMedicine
TopicEthics and Legal Issues in Pediatric Healthcare
Canadian institutionsMcGill University
FundersFonds de Recherche du Québec - Santé
KeywordsEthnographyCitizen journalismContext (archaeology)SociologyValue (mathematics)Affect (linguistics)Participatory action researchResistance (ecology)PsychologySocial psychologyAnthropologyPolitical scienceLaw

Abstract

fetched live from OpenAlex

Children with medical complexity have been defined within the literature as chronically ill and medically fragile children with complex care needs. Care for these children raises significant ethical and moral considerations. Therefore, this participatory ethnographic study conducted with eight children and their families aimed to better understand the moral experiences of children with medical complexity, based on views of children as moral agents and capable of understanding and expressing interpretations about their lived experiences. Through our participatory hermeneutical ethnographic research, we were able to shed light on how children with medical complexity express their moral experiences within a complex sociopolitical context, perpetuating dominant outlooks on what is considered a "normal" child. Children with medical complexity described their resistance to these dominant views as they strive to be included in discussions about matters that affect them, reacting to painful medical procedures and treatments, and expressing their concerns about their future aspirations. The knowledge advanced by this study about moral experiences of children with medical complexity can inform understandings of children's interests based on their own interpretations within complex sociopolitical contexts that value their lives differently.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score0.908

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 head0.069
GPT teacher head0.412
Teacher spread0.343 · 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

Citations5
Published2022
Admission routes2
Has abstractyes

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