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Record W3184481973 · doi:10.2147/jmdh.s317829

Understanding the Impact of Maternal HIV Infection on the Health and Well-Being of Mothers and Infants in South Africa: Siyakhula Collaborative Workshop Report

2021· article· en· W3184481973 on OpenAlexaff
Marina White, Ute Feucht, Louise D. V. du Toit, Theresa M. Rossouw, Kristin L. Connor

Bibliographic record

VenueJournal of Multidisciplinary Healthcare · 2021
Typearticle
Languageen
FieldMedicine
TopicBreastfeeding Practices and Influences
Canadian institutionsCarleton University
FundersUniversity of Pretoria
KeywordsBreastfeedingMedicinePsychological interventionObservational studyHuman immunodeficiency virus (HIV)Child healthFamily medicineEnvironmental healthPediatricsNursing

Abstract

fetched live from OpenAlex

The Siyakhula study is an ongoing, observational cohort study in Pretoria, South Africa, that aims to understand how maternal HIV infection and perinatal environmental factors shape development and health in infants who are HIV-exposed (in utero and during breastfeeding) but remain uninfected themselves (HEU). The Siyakhula Collaborative Workshop, which took place at the Research Centre for Maternal, Fetal, Newborn & Child Health Care Strategies at Kalafong Hospital in Pretoria, South Africa on November 15-16, 2018, brought together a group of international health scientists, clinicians, and stakeholders, including women with lived experience, to build capacity for research and training on the impact of HIV infection on women's and infants' health across geographical and disciplinary boundaries. The workshop sought to summarise the state of knowledge on the effects of being HEU on infant development and health in the first two years of life, identify gaps in existing research on modifiable exposures that may be associated with poor infant development, and develop ideas for novel research and interventions to lessen or prevent adverse health outcomes in pregnant or breastfeeding people living with HIV. These proceedings summarise the pre-workshop consensus process that was used to identify priority areas to discuss during small-group breakout sessions, as well as the themes and key challenges that emerged from these discussions during the workshop.

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 score0.029
Threshold uncertainty score0.260

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 integrity0.0000.001
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 head0.074
GPT teacher head0.380
Teacher spread0.306 · 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

Citations4
Published2021
Admission routes1
Has abstractyes

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