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Record W1954674958 · doi:10.1007/s10461-015-1109-8

Economic Evaluation of Community-Based HIV Prevention Programs in Ontario: Evidence of Effectiveness in Reducing HIV Infections and Health Care Costs

2015· article· en· W1954674958 on OpenAlexafffundabout
Stephanie Choi, David R. Holtgräve, Jean Bacon, Joanne Lush, Frank McGee, George Tomlinson, Sean B. Rourke

Bibliographic record

VenueAIDS and Behavior · 2015
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsSt. Michael's HospitalPublic Health OntarioUniversity of TorontoUniversity Health NetworkMinistry of Health and Long Term CareOntario AIDS NetworkOntario HIV Treatment Network
FundersOntario Ministry of Health and Long-Term CareOntario HIV Treatment NetworkPublic Health AgencyPublic Health Agency of Canada
KeywordsPublic healthHealth psychologyHuman immunodeficiency virus (HIV)Environmental healthHealth careCost effectivenessMedicineLiberian dollarProgram evaluationInvestment (military)Community healthBusinessGerontologyEconomic growthFamily medicineNursingPolitical scienceFinanceEconomics

Abstract

fetched live from OpenAlex

Investments in community-based HIV prevention programs in Ontario over the past two and a half decades are assumed to have had an impact on the HIV epidemic, but they have never been systematically evaluated. To help close this knowledge gap, we conducted a macro-level evaluation of investment in Ontario HIV prevention programs from the payer perspective. Our results showed that, from 1987 to 2011, province-wide community-based programs helped to avert a total of 16,672 HIV infections, saving Ontario's health care system approximately $6.5 billion Canadian dollars (range 4.8-7.5B). We also showed that these community-based HIV programs were cost-saving: from 2005 to 2011, every dollar invested in these programs saved about $5. This study is an important first step in understanding the impact of investing in community-based HIV prevention programs in Ontario and recognizing the impact that these programs have had in reducing HIV infections and health care costs.

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)0.002
metaresearch head score (Gemma)0.000
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 score0.195
Threshold uncertainty score0.858

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0000.000
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 head0.195
GPT teacher head0.453
Teacher spread0.258 · 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

Citations13
Published2015
Admission routes3
Has abstractyes

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