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Record W2095435617 · doi:10.1016/j.aogh.2015.02.585

The global health initiative and ACCESS Uganda partnership program: Developing health seminars for community health workers and evaluating nutritional knowledge and education practices in rural Uganda

2015· article· en· W2095435617 on OpenAlexaff
Richa Jain, Kate Dewar, B. Schwartzentruber, S. Ruscheinsky, Veronica Kapoor, James Sewanyana, Robert Kalyesubula

Bibliographic record

VenueAnnals of Global Health · 2015
Typearticle
Languageen
FieldMedicine
TopicGlobal Health and Surgery
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsGeneral partnershipCommunity health workersEconomic growthRural healthHealth educationDeveloping countryCommunity healthEnvironmental healthMedicinePolitical scienceNursingHealth careHealth servicesPublic healthPopulation

Abstract

fetched live from OpenAlex

The median (IQR) journal impact factor was 3 (1.9-4.7), and publications were cited 8 (3-19) times, even though many were published so recently that there have been few citation opportunities to date.Alumni who were postdoctoral Fellows (p < 0.001), from LMICs (p < 0.001), and were supported in earlier Program years (p¼0.003)had higher publication outputs, compared to doctoral Scholars, Americans, and later Program trainees.Other demographic factors (e.g., sex), duration of support (1 vs. 2 years), and research topics (e.g., infectious vs. non-communicable diseases) had little association with publication output except for trainees with research topics involving children, who had on average fewer publications (p¼0.003).Going Forward: The concentrated, mentored clinical research training in global health settings provided by the FICRS-F Program produced significant research productivity from its alumni.Program output grows each year, as alumni develop mature research careers and continue to publish.Publications of doctoral Scholars are likely to increase as they complete additional training and enter career positions.In 2012, FICRS-F was decentralized among 20 institutions in five consortia (Fogarty Global Health Program for Fellows and Scholars), emphasizing postdoctoral trainees from the US.Our results, particularly the finding that LMIC citizens had higher publication numbers than did US trainees, may inform the future evolution of the Program.

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)0.013
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.517
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.348
GPT teacher head0.588
Teacher spread0.240 · 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 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
Published2015
Admission routes1
Has abstractyes

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