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Record W4411045482 · doi:10.2196/68811

Association of Self-Rated Health in Pregnancy With Maternal Childhood Experiences, Socioeconomic Status, Parity, and Choice of Antenatal Care Providers: Cross-Sectional Study

2025· article· en· W4411045482 on OpenAlexvenueno aff
Bjarne Austad, Gunnhild Åberge Vie, Mari Bergan Hansen, Hanna Sandbakken Mørkved, Linn Getz, Bente Prytz Mjølstad

Bibliographic record

VenueJMIR Formative Research · 2025
Typearticle
Languageen
FieldMedicine
TopicMaternal Mental Health During Pregnancy and Postpartum
Canadian institutionsnot available
Fundersnot available
KeywordsSocioeconomic statusMedicineCross-sectional studyPregnancyLogistic regressionOddsSelf-rated healthOdds ratioHealth careParity (physics)Family medicineEnvironmental healthGerontologyPopulation

Abstract

fetched live from OpenAlex

Background: During pregnancy, self-rated health (SRH) and self-rated mental health (SRMH) are key indicators of health status and predictors of future health care needs. The relationship between pregnant women's health perceptions and their choice of antenatal care providers, midwives, or general practitioners (GPs) is not known. Factors like childhood experiences and socioeconomic status are important determinants of health throughout life. Understanding these health determinants can help health care providers better address the diverse needs of pregnant women. Objective: This study aims to assess how SRH and SRMH during pregnancy are associated with maternal childhood experiences, socioeconomic status, parity, and antenatal care provided by midwives or GPs. Methods: An anonymous, web-based cross-sectional survey was conducted from January to March 2022 among pregnant women in Norway, distributed via Facebook and Instagram. The survey included questions on SRH, SRMH, socioeconomic status, childhood perceptions, and antenatal program participation. Pearson's chi-squared test and logistic regression models were used to explore associations and estimate odds ratios for good SRH and SRMH. Results: Among 1402 participants, 94.7% (1328/1402) reported good or very good health before pregnancy, dropping to 67.8% (950/1402) during pregnancy (P<.001). Reporting your childhood as good was associated with better SRH compared with those who reported average or difficult childhood (70.2% [755/1076] vs 64% [114/178] vs 53.2% [74/139]; P<.001). This corresponds to 48% lower odds of good SRH for those reporting a difficult childhood compared to those reporting a good childhood (OR 0.52, 95% CI 0.36-0.76). Financial security and higher education were associated with better SRH (both P<.001). First-time mothers reported better SRH than those with previous births (73.9% [533/722] vs 61.4% [417/680]; P<.001). For SRMH, 89.9% (1260/1402) reported good or very good SRMH before pregnancy, decreasing to 73.1% (1024/1401) during pregnancy (P<.001). Women who reported a good childhood, financial security, higher education, and first-time mothers reported better SRMH during pregnancy (P<.001 for all). Nearly all women participated in the antenatal program, regardless of their subjective health, and most expressed satisfaction. Among participants, 55.6% (753/1354) received shared antenatal care, 38.6% (520/1354) were seen only by midwives, and 6% (81/1354) only by GPs. The proportion of women receiving antenatal care solely from a midwife decreased with declining SRH, from 42.6% (78/183) among those with very good SRH to 27.3% (15/55) among those with poor SRH. Conclusions: A difficult maternal childhood, low socioeconomic status, and having given birth before were associated with poorer SRH and SRMH during pregnancy. Both midwives and GPs played vital roles in providing antenatal care, though few women received antenatal care exclusively from GPs. The likelihood of physician involvement in care increased slightly with worsening health.

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)0.002
metaresearch head score (Gemma)0.003
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.025
GPT teacher head0.409
Teacher spread0.384 · 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
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".

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Citations4
Published2025
Admission routes1
Has abstractyes

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Same venueJMIR Formative Research→Same topicMaternal Mental Health During Pregnancy and Postpartum→French-language works237,207→