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Record W2907218307 · doi:10.32674/jump.v2i1.46

The Benefits of International Student Perspectives in a Global Cancer Workshop

2018· article· en· W2907218307 on OpenAlexaff
Molly Sweeney‐Magee, Ace Chan, M. Angelica Leon E., Narsis Afghari

Bibliographic record

VenueJournal of Underrepresented & Minority Progress · 2018
Typearticle
Languageen
FieldHealth Professions
TopicSchool Health and Nursing Education
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsDiversity (politics)Cultural diversityCancer preventionGlobal healthPublic healthPerspective (graphical)Medical educationGraduate studentsPublic relationsPolitical scienceCancerPsychologyMedicineNursingComputer science

Abstract

fetched live from OpenAlex

A recent opportunity to facilitate a faculty and student workshop in Global Health and Cancer Prevention allowed us, an international group of graduate students, to reflect on cancer disparities in our home countries as well as our understanding of these differences. This included discussing the complexity of achieving equitable cancer prevention globally, and learning from our shared and disparate experiences of public health systems across the world. It became clear through this process that being an international student, and working with other international students with distinct backgrounds, can result in an enriched learning environment. Our discussions highlighted gaps in our knowledge regarding other cultures, and gave each of us a new perspective on aspects of our own cultures, related to cancer, cancer- related risk factors, and more broadly. This forum demonstrated to us the benefits of the diversity international students can bring to the learning space.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.313

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 science0.0010.000
Research integrity0.0000.001
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 head0.094
GPT teacher head0.517
Teacher spread0.423 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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