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Record W4210716530 · doi:10.1093/cid/ciac102

The International Sexual Health And REproductive Health during COVID-19 (I-SHARE) Study: A Multicountry Analysis of Adults from 30 Countries Prior to and During the Initial Coronavirus Disease 2019 Wave

2022· review· en· W4210716530 on OpenAlexaff
Jennifer Toller Erausquin, Rayner Kay Jin Tan, Maximiliane Uhlich, Joel M Francis, Navin Kumar, Linda Campbell, Wei‐Hong Zhang, Takhona Grace Hlatshwako, Priya Kosana, Sonam Shah, Erica M. Brenner, Lore Remmerie, Aamirah Mussa, Kateřina Klapilová, Kristen P. Mark, Gabriela Perotta, Amanda Gabster, Edwin Wouters, Sharyn Burns, Jacqueline Hendriks, Devon J. Hensel, Simukai Shamu, Jenna Marie Strizzi, Tammary Esho, Chelsea Morroni, Stefano Eleuteri, Norhafiza Sahril, Wah Yun Low, Leona Plášilová, Gunta Lazdāne, Michael Marks, Adesola Olumide, Amr Abdelhamed, Alejandra López Gómez, Kristien Michielsen, Caroline Moreau, Joseph D. Tucker, Adedamola Adebayo, Emmanuel Adebayo, Noor Ani Ahmad, Nicolas D. Brunet, Anna Kågesten, Elizabeth Kemigisha, Eneyi E. Kpokiri, Ismaël Maatouk, Griffins Manguro, Filippo Maria Nimbi, Pedro Nobre, Caitlin Alsandria O’Hara, Oloruntomiwa Oyetunde, Muhd Hafizuddin Taufik Ramli, Dace Rezeberga, Juan Carlos Rivillas, Kun Tang, Ines Tavares

Bibliographic record

VenueClinical Infectious Diseases · 2022
Typereview
Languageen
FieldMedicine
TopicCOVID-19 Impact on Reproduction
Canadian institutionsWestern University
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute of Allergy and Infectious DiseasesNational Institute of Mental HealthNational Institutes of Health
KeywordsMedicineCoronavirus disease 2019 (COVID-19)Reproductive health2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)CoronavirusCoronavirus InfectionsPandemicBetacoronavirusVirologyDiseaseEnvironmental healthInfectious disease (medical specialty)Internal medicineOutbreakPopulation

Abstract

fetched live from OpenAlex

BACKGROUND: There is limited evidence to date about changes to sexual and reproductive health (SRH) during the initial wave of coronavirus disease 2019 (COVID-19). To address this gap, our team organized a multicountry, cross-sectional online survey as part of a global consortium. METHODS: Consortium research teams conducted online surveys in 30 countries. Sampling methods included convenience, online panels, and population-representative. Primary outcomes included sexual behaviors, partner violence, and SRH service use, and we compared 3 months prior to and during policy measures to mitigate COVID-19. We conducted meta-analyses for primary outcomes and graded the certainty of the evidence. RESULTS: Among 4546 respondents with casual partners, condom use stayed the same for 3374 (74.4%), and 640 (14.1%) reported a decline. Fewer respondents reported physical or sexual partner violence during COVID-19 measures (1063 of 15 144, 7.0%) compared to before COVID-19 measures (1469 of 15 887, 9.3%). COVID-19 measures impeded access to condoms (933 of 10 790, 8.7%), contraceptives (610 of 8175, 7.5%), and human immunodeficiency virus/sexually transmitted infection (HIV/STI) testing (750 of 1965, 30.7%). Pooled estimates from meta-analysis indicate that during COVID-19 measures, 32.3% (95% confidence interval [CI], 23.9%-42.1%) of people needing HIV/STI testing had hindered access, 4.4% (95% CI, 3.4%-5.4%) experienced partner violence, and 5.8% (95% CI, 5.4%-8.2%) decreased casual partner condom use (moderate certainty of evidence for each outcome). Meta-analysis findings were robust in sensitivity analyses that examined country income level, sample size, and sampling strategy. CONCLUSIONS: Open science methods are feasible to organize research studies as part of emergency responses. The initial COVID-19 wave impacted SRH behaviors and access to services across diverse global settings.

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)0.009
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.023
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.007
Bibliometrics0.0030.005
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.161
GPT teacher head0.504
Teacher spread0.343 · 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 designObservational
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

Citations35
Published2022
Admission routes1
Has abstractyes

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