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Record W3201502729 · doi:10.1080/00224499.2021.1970708

Variable Sexual Satisfaction in Pregnancy: A Latent Profile Analysis of Pregnant Wives and Their Husbands

2021· article· en· W3201502729 on OpenAlexaff
David B. Allsop, Chelom E. Leavitt, Jeremy B. Yorgason, Erin K. Holmes

Bibliographic record

VenueThe Journal of Sex Research · 2021
Typearticle
Languageen
FieldMedicine
TopicMaternal Mental Health During Pregnancy and Postpartum
Canadian institutionsDalhousie University
Fundersnot available
KeywordsPregnancyBiopsychosocial modelPsychologyDemographyAbortionClinical psychologyDevelopmental psychologyMedicinePsychiatry

Abstract

fetched live from OpenAlex

Although not all couples achieve high levels of sexual satisfaction during pregnancy, evidence of variability in couple sexual satisfaction during pregnancy indicates that sexual dissatisfaction in pregnancy does not apply to all. Subsequently, the current study examined whether a nationally representative U.S. sample of wives and husbands (N = 523 couples) fell into subgroups in terms of their sexual satisfaction during pregnancy and to what degree biopsychosocial factors distinguish potential subgroups. Latent profile analyses, adjusted for pregnancy-related biological factors, indicated that couples could be classified into two subsets – a larger subset of couples where wives and husbands were satisfied with sex overall (79%) and a smaller subset where wives and husbands were neutral about satisfaction with sex (21%). Lower depressive symptoms among wives was associated with a greater likelihood of being in the more satisfied subset over the less satisfied subset – the only significant group membership predictor among a variety of other factors. Implications include notions that couples and practitioners should consider women’s depressive symptoms throughout pregnancy in addition to the perinatal period, and that most U.S. newly married pregnant couples do well navigating sexual satisfaction challenges during pregnancy.

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 score0.038
Threshold uncertainty score0.224

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.069
GPT teacher head0.376
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

Citations2
Published2021
Admission routes1
Has abstractyes

Explore more

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