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Record W2949313234 · doi:10.1080/08949468.2019.1603036

Engaging Northern Indigenous Children through Drawing for Community Health Research: A Picture of the Social Impact of <i>H. pylori</i> Infection in Fort McPherson in the Northwest Territories, Canada

2019· article· en· W2949313234 on OpenAlexfundaboutno aff
Megan J. Highet, Amy Colquhoun, Rachel Munday, Karen J. Goodman

Bibliographic record

VenueVisual Anthropology · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsnot available
FundersUniversity of Alberta
KeywordsIndigenousArcticSociologyKnowledge translationCommunity healthTraditional knowledgeMedicinePublic relationsPolitical scienceNursingPublic healthEcology

Abstract

fetched live from OpenAlex

An analysis of drawings made by Indigenous children in the Arctic hamlet of Fort McPherson, Canada, serves the dual purpose of contributing children’s perspectives to community-driven research on H. pylori infection, and demonstrating the utility of employing visual approaches for research involving school-aged children. Insights into their knowledge, attitudes, and experiences relating to this infection yielded important insights for knowledge translation with primary-care providers. Results may be utilized within the broader research program to improve approaches to delivering care for H. pylori infection, by helping to integrate community members’ perspectives into the ideologies of primary-care providers.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.318

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0260.012
Scholarly communication0.0040.001
Open science0.0020.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.362
GPT teacher head0.615
Teacher spread0.253 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations6
Published2019
Admission routes2
Has abstractyes

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