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Record W4280556256 · doi:10.1136/bmjgh-2022-008501

Global health education in high-income countries: confronting coloniality and power asymmetry

2022· review· en· W4280556256 on OpenAlexaff
Hoda E. Sayegh, Christina A. Harden, Hijab Khan, Madhukar Pai, Quentin Eichbaum, Charles Ibingira, Gelila Goba

Bibliographic record

VenueBMJ Global Health · 2022
Typereview
Languageen
FieldMedicine
TopicGlobal Health and Surgery
Canadian institutionsMcGill University
FundersJimma University
KeywordsPower (physics)Global healthPublic healthPolitical scienceEconomic growthDevelopment economicsEconomicsMedicineHealth careNursing

Abstract

fetched live from OpenAlex

Contemporary global health education is overwhelmingly skewed towards high-income countries (HICs). HIC-based global health curricula largely ignore colonial origins of global health to the detriment of all stakeholders, including trainees and affected community members of low- and middle-income countries. Using the Consortium of Universities for Global Health’s Global Health Education Competencies Tool-Kit , we analyse the current structure and content of global health curricula in HICs. We identify two major areas in global health education that demand attention: (1) the use of a competency-based education framework and (2) the shortcomings of curricular content. We propose actionable changes that challenge current power asymmetries in global health education.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.898
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.028
GPT teacher head0.455
Teacher spread0.427 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations46
Published2022
Admission routes1
Has abstractyes

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