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Record W2484953994 · doi:10.1111/1471-0528.14225

Perinatal mental health in low‐ and middle‐income country migrants

2016· letter· en· W2484953994 on OpenAlexaffabout
Donna E. Stewart

Bibliographic record

VenueBJOG An International Journal of Obstetrics & Gynaecology · 2016
Typeletter
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMental healthAnxietyMedicinePopulationDepression (economics)PsychiatryDomestic violenceEnvironmental healthPoison controlSuicide prevention

Abstract

fetched live from OpenAlex

Perinatal mental health disorders negatively affect the health of the mother, fetus, child and other family members and are now recognised as one of the most common disorders of childbearing. The commonest of these, perinatal depression and anxiety, affects approximately 13% of women in high-income countries (HIC), 15–20% of women in low- and middle-income countries (LMIC), and 42% of migrant women (Collins et al. Arch Womens Ment Health 2011;14:3–11). The world is currently in a migrant crisis with millions of individuals on the move or in emergency camps fleeing conflict, violence, natural disasters or seeking a better life. In fact, the number of international migrants has grown faster than the world's population. Many migrants are vulnerable women who may be pregnant or postpartum and face serious health, economic and social challenges. Unfortunately most research on perinatal mental disorders comes from HIC. Better information is urgently needed on migrant populations, especially from LMIC. In this issue of BJOG, Fellmeth et al. (BJOG 2016;DOI: 10.1111/1471-0528.14184) provide a timely, highly relevant, methodologically strong, systematic review of the literature on migration and perinatal mental health in women originating in LMIC. They analysed 40 studies of nearly 8000 migrant women across four continents and over 11 000 comparison women and found a pooled prevalence of 31% (95% CI 23–40%) for any depressive disorder and 17% (95% CI 12–23%) for major depressive disorder. Although smaller studies have shown elevated rates for anxiety and posttraumatic stress disorder in migrant women from LMIC (Gagnon et al. Soc Sci Med 2013;76:197–207), the current study found insufficient data to assess the burden of anxiety, posttraumatic stress disorder or psychosis in four relevant studies. Overall, migrant new mothers are vulnerable to higher rates of mental health disorders than nonmigrants in the destination country. As in studies of risk factors associated with poor perinatal mental health (in nonmigrant populations), the current review found that a family or personal history of poor mental health and lack of social support were common factors. Our small study of depressed migrant new mothers in Canada found that being separated from family, social isolation, feeling overwhelmed by changes, financial worries, poor knowledge of community services and language difficulties were often problematic. However, negative attitudes of healthcare and social services staff, stigma and fear of being labelled as unfit mothers were vital barriers to seeking care (Ahmad et al. Arch Womens Ment Health 2008;11:295–303). Given that perinatal migrant women, especially from LMIC, are at high risk for mental disorders, healthcare and social-service providers, as well as policy makers, should develop best practices for their care. Most importantly perinatal healthcare providers should be sensitive to the challenges of these women. Supportive inquiries about their coping, wellbeing and mood can assist in case identification and appropriate referrals to social or mental health services. Research on specific psychosocial interventions for this vulnerable population is urgently needed; but meanwhile those found effective in other populations to improve the physical and mental health of mothers and children, should be implemented. Full disclosure of interests available to view online as supporting information. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

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)0.005
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0040.003
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.023
GPT teacher head0.326
Teacher spread0.303 · 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 designNot applicable
Domainnot available
GenreCommentary

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
Published2016
Admission routes2
Has abstractyes

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