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Record W2789626719 · doi:10.1177/1049732318757489

Participatory Hermeneutic Ethnography: A Methodological Framework for Health Ethics Research With Children

2018· article· en· W2789626719 on OpenAlexafffund
Marjorie Montreuil, Franco A. Carnevale

Bibliographic record

VenueQualitative Health Research · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicChildren's Rights and Participation
Canadian institutionsMcGill University
FundersFonds de Recherche du Québec - SantéCanadian Nurses FoundationSocial Sciences and Humanities Research Council of CanadaMcGill University
KeywordsHermeneuticsEthnographySociologyParticipatory action researchMeaning (existential)Health careContext (archaeology)Qualitative researchCitizen journalismEthics of careResearch ethicsEngineering ethicsNursingEpistemologySocial scienceMedicinePsychologyPolitical scienceAnthropologyPsychotherapistLaw

Abstract

fetched live from OpenAlex

When conducting ethics research with children in health care settings, studying children's experiences is essential, but so is the context in which these experiences happen and their meaning. Using Charles Taylor's hermeneutic philosophy, we developed a methodological framework for health ethics research with children that bridges key aspects of ethnography, participatory research, and hermeneutics. This qualitative framework has the potential to offer rich data and discussions related to children as well as family members and health care workers' moral experiences in specific health care settings, while examining the institutional norms, structures, and practices and how they interrelate with experiences. Through a participatory hermeneutic ethnographic study, important ethical issues can be highlighted and examined in light of social/local imaginaries and horizons of significance, to address some of the ethical concerns that can be present in a specific health care setting.

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)0.201
metaresearch head score (Gemma)0.019
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesMetaresearch, Science and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.483
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.2010.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0100.007
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.948
GPT teacher head0.772
Teacher spread0.176 · 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; both teacher heads agree on what is shown here.

Study designTheoretical or conceptual
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

Citations53
Published2018
Admission routes2
Has abstractyes

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