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Record W2590839767 · doi:10.1097/qai.0000000000001329

The Continuum of HIV Care in Rural Communities in the United States and Canada: What Is Known and Future Research Directions

2017· review· en· W2590839767 on OpenAlexaffabout
Katherine R. Schafer, Helmut Albrecht, Rebecca Dillingham, Robert S. Hogg, Denise Jaworsky, Ken Kasper, Mona Loutfy, Lauren J MacKenzie, Kathleen A. McManus, Kris Ann Oursler, Scott D. Rhodes, Hasina Samji, Stuart Skinner, Christina J. Sun, Sharon Weissman, Michael Ohl

Bibliographic record

VenueJAIDS Journal of Acquired Immune Deficiency Syndromes · 2017
Typereview
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsUniversity of SaskatchewanHIV Legal NetworkWomen's College HospitalUniversity of TorontoSimon Fraser UniversityUniversity of ManitobaBC Centre for Disease ControlUniversity of British ColumbiaAIDS Vancouver
FundersNational Institute on AgingAgency for Healthcare Research and Quality
KeywordsSocioeconomic statusHuman immunodeficiency virus (HIV)Continuum of careContext (archaeology)Health careEconomic growthGeographyEnvironmental healthPolitical scienceMedicinePopulationFamily medicine

Abstract

fetched live from OpenAlex

The nature of the HIV epidemic in the United States and Canada has changed with a shift toward rural areas. Socioeconomic factors, geography, cultural context, and evolving epidemics of injection drug use are coalescing to move the epidemic into locations where populations are dispersed and health care resources are limited. Rural-urban differences along the care continuum demonstrate the implications of this sociogeographic shift. Greater attention is needed to build a more comprehensive understanding of the rural HIV epidemic in the United States and Canada, including research efforts, innovative approaches to care delivery, and greater community engagement in prevention and care.

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)0.003
metaresearch head score (Gemma)0.007
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: Review
Teacher disagreement score0.416
Threshold uncertainty score0.837

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.006
Science and technology studies0.0020.002
Scholarly communication0.0040.002
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.065
GPT teacher head0.390
Teacher spread0.325 · 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

Citations154
Published2017
Admission routes2
Has abstractyes

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