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Record W4311691724 · doi:10.1371/journal.pgph.0001361

Ethical implications of economic compensation for voluntary medical male circumcision for HIV prevention and epidemic control

2022· review· en· W4311691724 on OpenAlexaff
Johannes Köhler, Jerome Amir Singh, Stuart Rennie, Julia Samuelson, Andreas Reis

Bibliographic record

VenuePLOS Global Public Health · 2022
Typereview
Languageen
FieldMedicine
TopicGenital Health and Disease
Canadian institutionsPublic Health OntarioUniversity of Toronto
FundersStiftung MercatorWorld Health OrganizationBundesministerium für Bildung und ForschungBill and Melinda Gates Foundation
KeywordsMale circumcisionHuman immunodeficiency virus (HIV)TurnoverPsychological interventionCompensation (psychology)MedicineRisk compensationEnvironmental healthBusinessPsychologyEconomicsSocial psychologyNursingFamily medicinePopulationHealth services

Abstract

fetched live from OpenAlex

Despite tremendous efforts in fighting HIV over the last decades, the estimated annual number of new infections is still a staggering 1.5 million. There is evidence that voluntary medical male circumcision (VMMC) provides protection against men's heterosexual acquisition of HIV-1 infection. Despite good progress, most countries implementing VMMC for HIV prevention programmes are challenged to reach VMMC coverage rates of 90%. Particularly for men older than 25 years, a low uptake has been reported. Consequently, there is a need to identify, study and implement interventions that could increase the uptake of VMMC. Loss of income and incurred transportation costs have been reported as major barriers to uptake of VMMC. In response, it has been suggested to use economic compensation in order to increase VMMC uptake. In this discussion paper, we present and review relevant arguments and concerns to inform decision-makers about the ethical implications of using economic compensation, and to provide a comprehensive basis for policy and project-related discussions and decisions.

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)0.003
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.959
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.165
GPT teacher head0.454
Teacher spread0.290 · 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 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

Citations14
Published2022
Admission routes1
Has abstractyes

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