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Record W2098897698 · doi:10.1177/1363461513501712

Mood and anxiety problems in perinatal Indigenous women in Australia, New Zealand, Canada, and the United States: A critical review of the literature

2013· review· en· W2098897698 on OpenAlexaffabout
Angela Bowen, Vicky Duncan, Shelley Peacock, Rudy Bowen, Laura E. Schwartz, Diane Campbell, Nazeem Muhajarine

Bibliographic record

VenueTranscultural Psychiatry · 2013
Typereview
Languageen
FieldMedicine
TopicMaternal Mental Health During Pregnancy and Postpartum
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsAnxietyIndigenousMental healthMoodClinical psychologyPsychologyPopulationPsychiatryPostpartum depressionDiversity (politics)Qualitative researchMedicinePregnancySociologyEnvironmental healthSocial science

Abstract

fetched live from OpenAlex

We conducted a review of research literature related to anxiety, depression, and mood problems in Indigenous women in Canada, the United States (including Hawaii), Australia, and New Zealand. Quantitative and qualitative research studies published between 1980 and March 2010 were reviewed. The initial search revealed 396 potential documents, and after being checked for relevance by two researchers, data were extracted from 16 quantitative studies, one qualitative research article, and one dissertation. Depression is a common problem in Indigenous pregnant and postpartum women; however, the prevalence and correlates of anxiety and mood disorders are understudied. The review identified four key areas where further research is needed: (a) longitudinal, population-based studies; (b) further validation and modification of appropriate screening tools; (c) exploration of cultural diversity and meaning of the lived experiences of antenatal and postpartum depression, anxiety, and mood disorders; and (d) development of evidence-informed practices for researchers and practitioners through collaborations with Aboriginal communities to better understand and improve mental health of women of childbearing age.

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)0.000
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: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.152
Threshold uncertainty score0.843

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.024
GPT teacher head0.313
Teacher spread0.289 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations33
Published2013
Admission routes2
Has abstractyes

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