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Record W4321370905 · doi:10.2196/43800

Prevalence and Associated Factors of Maternal Depression and Anxiety Among African Immigrant Women in Alberta, Canada: Quantitative Cross-sectional Survey Study

2023· article· en· W4321370905 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
KeywordsAnxietyDepression (economics)MedicineLogistic regressionCross-sectional studyEdinburgh Postnatal Depression ScaleImmigrationDemographyPostpartum depressionHospital Anxiety and Depression ScaleMental healthPsychiatryClinical psychologyPregnancyInternal medicineDepressive symptomsGeography

Abstract

fetched live from OpenAlex

BACKGROUND: Although there is a significant body of evidence on maternal mental health, an inadequate focus has been placed on African immigrant women. This is a significant limitation given the rapidly changing demographics in Canada. The prevalence of maternal depression and anxiety among African immigrant women in Alberta and Canada, as well as the associated risk factors, are not well understood and remain largely unknown. OBJECTIVE: The purpose of this study was to investigate the prevalence and associated factors of maternal depression and anxiety among African immigrant women living in Alberta, Canada up to 2 years postpartum. METHODS: This cross-sectional study surveyed 120 African immigrant women within 2 years of delivery in Alberta, Canada from January 2020 to December 2020. The English version of the Edinburgh Postnatal Depression Scale-10 (EPDS-10), the Generalized Anxiety Disorder-7 (GAD-7) scale, and a structured questionnaire regarding associated factors were administered to all participants. A cutoff score of 13 on the EPDS-10 was indicative of depression, while a cutoff score of 10 on the GAD-7 scale was indicative of anxiety. Multivariable logistic regression was used to determine the factors significantly associated with maternal depression and anxiety. RESULTS: Among the 120 African immigrant women, 27.5% (33/120) met the EPDS-10 cutoff score for depression and 12.1% (14/116) met the GAD-7 cutoff score for anxiety. The majority of respondents with maternal depression were younger (18/33, 56%), had a total household income of CAD $60,000 or more (US $45,000 or more; 21/32, 66%), rented their homes (24/33, 73%), had an advanced degree (19/33, 58%), were married (26/31, 84%), were recent immigrants (19/30, 63%), had friends in the city (21/31, 68%), had a weak sense of belonging in the local community (26/31, 84%), were satisfied with their settlement process (17/28, 61%), and had access to a regular medical doctor (20/29, 69%). In addition, the majority of respondents with maternal anxiety were nonrecent immigrants (9/14, 64%), had friends in the city (8/13, 62%), had a weak sense of belonging in the local community (12/13, 92%), and had access to a regular medical doctor (7/12, 58%). The multivariable logistic regression model identified demographic and social factors significantly associated with maternal depression (maternal age, working status, presence of friends in the city, and access to a regular medical doctor) and maternal anxiety (access to a regular medical doctor and sense of belonging in the local community). CONCLUSIONS: Social support and community belonging initiatives may improve the maternal mental health outcomes of African immigrant women. Given the complexities immigrant women face, more research is needed on a comprehensive approach for public health and preventive strategies regarding maternal mental health after migration, including increasing access to family doctors.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.037
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.060
GPT teacher head0.404
Teacher spread0.345 · 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

Citations5
Published2023
Admission routes3
Has abstractyes

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