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Record W2806364473 · doi:10.1186/s12982-018-0076-8

The contributions and future direction of Program Science in HIV/STI prevention

2018· article· en· W2806364473 on OpenAlexaff
Marissa Becker, Sharmistha Mishra, Sevgi O. Aral, Parinita Bhattacharjee, Rob Lorway, Kalada Green, John Anthony, Shajy Isac, Faran Emmanuel, Helgar Musyoki, Lisa Lazarus, Laura H. Thompson, Eve Cheuk, James Blanchard

Bibliographic record

VenueEmerging Themes in Epidemiology · 2018
Typearticle
Languageen
FieldHealth Professions
TopicAdolescent Sexual and Reproductive Health
Canadian institutionsUniversity of TorontoSt. Michael's HospitalUniversity of ManitobaManitoba Health
Fundersnot available
KeywordsHuman immunodeficiency virus (HIV)Public healthEpidemiologyEnvironmental healthMedicineData scienceVirologyManagement scienceComputer scienceEngineeringPathology

Abstract

fetched live from OpenAlex

BACKGROUND: Program Science is an iterative, multi-phase research and program framework where programs drive the scientific inquiry, and both program and science are aligned towards a collective goal of improving population health. DISCUSSION: To achieve this, Program Science involves the systematic application of theoretical and empirical knowledge to optimize the scale, quality and impact of public health programs. Program Science tools and approaches developed for strategic planning, program implementation, and program management and evaluation have been incorporated into HIV and sexually transmitted infection prevention programs in Kenya, Nigeria, India, and the United States. CONCLUSION: In this paper, we highlight key scientific contributions that emerged from the growing application of Program Science in the field of HIV and STI prevention, and conclude by proposing future directions for Program Science.

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)0.121
metaresearch head score (Gemma)0.136
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.121
Threshold uncertainty score0.640

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1210.136
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0060.006
Science and technology studies0.0050.030
Scholarly communication0.0160.019
Open science0.0030.008
Research integrity0.0080.011
Insufficient payload (model declined to judge)0.0070.001

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 head0.113
GPT teacher head0.537
Teacher spread0.424 · 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 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

Citations22
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueEmerging Themes in EpidemiologySame topicAdolescent Sexual and Reproductive HealthFrench-language works237,207