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Record W2145099765 · doi:10.1086/598199

Resurrecting the Triple Threat: Academic Social Responsibility in the Context of Global Health Research

2009· article· en· W2145099765 on OpenAlexaff
Yukari C. Manabe, Shevin T. Jacob, David B. Thomas, Thomas C. Quinn, Allan Ronald, Alex Coutinho, Harriet Mayanja‐Kizza, Concepta Merry

Bibliographic record

VenueClinical Infectious Diseases · 2009
Typearticle
Languageen
FieldMedicine
TopicGlobal Health and Surgery
Canadian institutionsUniversity of Manitoba
FundersNational Institute of Allergy and Infectious DiseasesNational Heart, Lung, and Blood InstituteNational Institutes of Health
KeywordsMedicineWorkforceContext (archaeology)Public relationsPandemicGlobal healthHealth careHuman immunodeficiency virus (HIV)Human resourcesEconomic growthNursingMedical educationPolitical sciencePublic healthFamily medicineCoronavirus disease 2019 (COVID-19)Infectious disease (medical specialty)Disease

Abstract

fetched live from OpenAlex

As a result of the pandemic of human immunodeficiency virus infection, more academic physicians involved in research are working in resource-limited settings, especially in the field of infectious diseases. These researchers are often located in close proximity to health care facilities with serious workforce shortages. Because institutions and funders support global health research, they have the opportunity to make a lasting impact on the health system by training local health workers where the research is being conducted. Academic researchers who spend clinical time in local health care centers and who teach and mentor students as part of academic social responsibility will build capacity, an investment that will yield dividends for future generations.

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)0.150
metaresearch head score (Gemma)0.102
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.150
Threshold uncertainty score0.796

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1500.102
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.002
Science and technology studies0.0480.109
Scholarly communication0.0460.030
Open science0.0040.059
Research integrity0.0200.029
Insufficient payload (model declined to judge)0.0080.001

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 head0.220
GPT teacher head0.563
Teacher spread0.343 · 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 designTheoretical or conceptual
Domainnot available
GenreCommentary

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

Citations27
Published2009
Admission routes1
Has abstractyes

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