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Record W2552623512 · doi:10.1177/0017896916676209

Cross-cultural school-based encounters as global health education

2016· article· en· W2552623512 on OpenAlexaff
Maria Bruselius-Jensen, Kerry Renwick, Jens Aagaard‐Hansen

Bibliographic record

VenueHealth Education Journal · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Competency in Health Care
Canadian institutionsUniversity of British Columbia
FundersWorld Health Organization
KeywordsCross-culturalPsychologySociologyPedagogyEnvironmental healthMedicineAnthropology

Abstract

fetched live from OpenAlex

Objective: Drawing on the concepts of the cosmopolitan person and democratic health education, this article explores the merits of primary school–based, cross-cultural dialogues for global health education. Design: A qualitative study of the learning outcomes of the Move|Eat|Learn (MEL) project. MEL facilitates cultural meetings, primarily Skype-based, between students from Kenya and Denmark, with the aim of promoting reflection on differences and similarities in everyday living conditions and their impact on health practices. Setting: Three Danish and one Kenyan primary schools. Methods: Qualitative analysis of 18 focus group discussions with 72 Danish and 36 Kenyan students. Results: Cross-cultural dialogues promoted students’ engagement and reflections on their own and peers’ health condition, access to education, food cultures, gender and family structures. Conclusion: Findings indicate the merits of cross-cultural dialogues as a means of educating students to become global health agents with a cosmopolitan outlook.

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)0.018
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.095

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0120.024
Scholarly communication0.0080.006
Open science0.0010.018
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0070.000

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 head0.041
GPT teacher head0.494
Teacher spread0.453 · 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 designNot applicable
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
Published2016
Admission routes1
Has abstractyes

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