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Record W3114125487 · doi:10.1177/1367493520976300

Children’s assent within clinical care: A concept analysis

2020· article· en· W3114125487 on OpenAlexafffund
Marjorie Montreuil, Justine Fortin, Éric Racine

Bibliographic record

VenueJournal of Child Health Care · 2020
Typearticle
Languageen
FieldMedicine
TopicEthics and Legal Issues in Pediatric Healthcare
Canadian institutionsMontreal Clinical Research InstituteMcGill University
FundersInstitut de Recherche Clinique De Montréal
KeywordsHarmInstrumentalismAutonomyPsychologyClinical judgmentHealth careProcess (computing)Social psychologyMedicinePolitical scienceLawEpistemology

Abstract

fetched live from OpenAlex

Seeking children's assent has been put forward as a way to foster children's involvement in the healthcare decision-making process. However, the functions of the concept of assent within clinical care are manifold, and methods used to recognize children's capacities and promote their involvement in their care remain debated. We performed an instrumentalist concept analysis of assent, with 58 included articles. Final themes were jointly identified through a deliberative process. Two distinct perspectives of assent were predominant: as an affirmative agreement for a specific decision and as part of a continuous, interactive process of care. Differing standards were provided as to how and when to apply the concept of assent. The concept of dissent was largely omitted from conceptions of assent, especially in situations for which children's refusal would lead to severe health consequences. Ethical implications included fostering autonomy, reducing physical/psychological harm to the child, respecting the child as a human being, and fulfilling the universal rights of the child. There remain important gaps in the theory of assent and its desirable and possible practical implications. Practical standards are largely missing, and evidence supporting the claims made in the literature requires further investigation.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.582
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.003
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 head0.040
GPT teacher head0.432
Teacher spread0.391 · 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 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

Citations11
Published2020
Admission routes2
Has abstractyes

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