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Defining Male Support During and After Pregnancy From the Perspective of HIV‐Positive and HIV‐Negative Women in Durban, South Africa

2011· article· en· W2121750771 on OpenAlexaboutno aff
Suzanne Maman, Dhayendre Moodley, Allison K. Groves

Bibliographic record

VenueJournal of Midwifery & Women s Health · 2011
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsnot available
FundersEunice Kennedy Shriver National Institute of Child Health and Human Development
KeywordsSocial supportPsychosocialMedicinePerspective (graphical)PregnancyHuman immunodeficiency virus (HIV)Quarter (Canadian coin)Postpartum periodPublic healthFamily medicinePsychologyPsychiatryNursingSocial psychology

Abstract

fetched live from OpenAlex

INTRODUCTION: Greater male support during pregnancy and in the postpartum period may improve health outcomes for mothers and children. To develop effective strategies to engage men, we need to first understand the ways that men are currently engaged and the barriers to their greater involvement. METHODS: We conducted in-depth interviews in isiZulu with 30 HIV-positive women and 16 HIV-negative women who received prenatal care from a public clinic in Durban, South Africa. Interviews were audiotaped, transcribed, translated, and coded for analysis. RESULTS: Although less than a quarter of women reported that their partners accompanied them to the clinic, they described receiving other material and psychosocial support from partners. More HIV-positive women reported that their partners were not involved or not supportive, and in some cases direct threats and experiences with violence caused them to fear partner involvement. DISCUSSION: We need to broaden the lens through which we consider male support during pregnancy and in the postpartum period and acknowledge that male involvement may not always be in the best interest of women. Engaging supportive partners outside of the clinic setting and incorporating other important social network members are important next steps in the effort to increase support for women.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.483
Threshold uncertainty score0.827

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.022
GPT teacher head0.292
Teacher spread0.271 · 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

Citations48
Published2011
Admission routes1
Has abstractyes

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