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Exploring the Hidden Curriculum of Global Health

2014· article· en· W1563226169 on OpenAlexaff
Kelly Anderson, Danyaal Raza, Jane Philpott

Bibliographic record

VenueSocial medicine · 2014
Typearticle
Languageen
FieldMedicine
TopicGlobal Health and Surgery
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCurriculumHumanitiesSociologyPedagogyPhilosophy

Abstract

fetched live from OpenAlex

Universities are experiencing a hurried expansion of global health programs to accommodate interested trainees. A growing body of literature has addressed the practical and ethical considerations for singular global health experiences, and other articles have begun to tackle competencies for building global health curricula. However, standardized approaches to teaching global health are frequently absent, leaving learners to build their knowledge through a variety of avenues: formal coursework, informal reading, conferences, research, mentorship, and electives. The hidden curriculum, described as “processes, pressures and constraints which fall outside…the formal curriculum, and which are often unarticulated or unexplored”, has been identified as a powerful force in medical education, affecting impressions, decisions, career paths and morale of trainees. Because global health education is evolving rapidly, is it possible it contains its own uncharted hidden curriculum influencing learners in unknown ways? By investigating the contents of the hidden curricula, trainees have the opportunity to reframe and reconsider how it affects them, whether positively or negatively. But identifying and articulating hidden curricula or shared hidden perceptions is not an easy task. We offer four areas of hidden curriculum as opportunities for exploration.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.760
Threshold uncertainty score0.308

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.069
GPT teacher head0.371
Teacher spread0.302 · 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.

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

Citations5
Published2014
Admission routes1
Has abstractyes

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