MétaCan
Menu
← Back to cohort
Record W4318263996 · doi:10.2196/40008

African Immigrant Mothers’ Views of Perinatal Mental Health and Acceptability of Perinatal Mental Health Screening: Quantitative Cross-sectional Survey Study

2023· article· en· W4318263996 on OpenAlexaffvenueabout
Chinenye Nmanma Nwoke, Olu Awosoga, Sheila McDonald, Glenda Tibe Bonifacio, Brenda Leung

Bibliographic record

VenueJMIR Formative Research · 2023
Typearticle
Languageen
FieldMedicine
TopicMaternal Mental Health During Pregnancy and Postpartum
Canadian institutionsUniversity of CalgaryUniversity of Lethbridge
Fundersnot available
KeywordsMental healthMedicineCross-sectional studyAnxietyContext (archaeology)ImmigrationScale (ratio)Postnatal CareAffect (linguistics)Family medicinePsychiatryPregnancyPsychologyGeography

Abstract

fetched live from OpenAlex

BACKGROUND: Mental health disorders are the most common perinatal conditions. They affect mothers, babies, partners, and support networks. However, <15% of pregnant and postpartum women seek timely help for their mental health care. Low perinatal mental health knowledge and universal screening unacceptability are cited as important deterrents to obtaining timely mental health care. OBJECTIVE: The purpose of this quantitative cross-sectional study was 2-fold: (1) to determine African immigrant mothers' views of perinatal mental health and to identify predictors of those views and (2) to identify African immigrant mothers' views regarding perinatal mental health screening and to determine factors associated with those views. METHODS: A cross-sectional survey was conducted using a convenience sample of African immigrant women from the province of Alberta, Canada. Respondents were eligible to participate if they were aged ≥18 years, had a live birth, and the infant was aged ≤2 years. Questions were drawn from the Edinburgh Postnatal Depression Scale, the Generalized Anxiety Disorder-7 scale, and additional questions were developed using the Alberta Maternal Mental Health 2012 survey as a guide and tested to reflect the immigrant context. Descriptive and multivariable regression analyses were conducted. RESULTS: Among the 120 respondents, 46.5% (53/114) were aged 31-35 years, 76.1% (89/117) were employed or on maternity leave, 92.5% (111/120) were married, and 55.6% (65/117) had younger infants aged 0 to 12 months. Significantly more respondents had higher levels of knowledge of postnatal (109/115, 94.8%) than prenatal (57/110, 51.2%) mental health (P<.001). Only 25.4% (28/110) of the respondents accurately identified that prenatal anxiety or depression could negatively impact child development. Personal knowledge of postpartum anxiety and depression was a significant predictor of prenatal and postnatal mental health knowledge. Most respondents strongly agreed or agreed that all women should be screened in the prenatal (82/109, 75.2%) and postnatal (91/110, 82.7%) periods. Respondents reported that their partner would be their first choice when seeking help and support. The acceptability of postnatal screening was a significant predictor of prenatal mental health knowledge (P<.001), whereas the acceptability of prenatal screening was a significant predictor of postnatal mental health knowledge (P=.03). Prenatal mental health knowledge was a significant predictor of both prenatal (P<.001) and postnatal (P=.001) screening acceptability. CONCLUSIONS: Although African mothers' knowledge of postnatal mental health is high, their prenatal mental health knowledge and its influence on child development are limited. Perinatal mental health interventions for African immigrant mothers in Alberta should target these knowledge gaps. The high acceptability of universal perinatal mental health screening among African mothers provides a promising strategy for perinatal mental health literacy initiatives to achieve optimal perinatal mental 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)0.001
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 score0.096
Threshold uncertainty score0.190

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.182
GPT teacher head0.507
Teacher spread0.325 · 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".

Quick stats

Citations9
Published2023
Admission routes3
Has abstractyes

Explore more

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