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Record W1973206759 · doi:10.1177/1363459314554315

Postpartum depression in refugee and asylum-seeking women in Canada: A critical health psychology perspective

2014· article· en· W1973206759 on OpenAlexaffabout
Amy Brown‐Bowers, Kelly McShane, Karline Wilson‐Mitchell, Maria Gurevich

Bibliographic record

VenueHealth An Interdisciplinary Journal for the Social Study of Health Illness and Medicine · 2014
Typearticle
Languageen
FieldMedicine
TopicMaternal Mental Health During Pregnancy and Postpartum
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsRefugeePostpartum depressionMainstreamDistressConceptualizationCritical appraisalPsychologyPerspective (graphical)PsychiatryDepression (economics)MedicineClinical psychologyPolitical scienceAlternative medicinePregnancy

Abstract

fetched live from OpenAlex

Canada has one of the world's largest refugee resettlement programs in the world. Just over 48 percent of Canadian refugees are women, with many of them of childbearing age and pregnant. Refugee and asylum-seeking women in Canada face a five times greater risk of developing postpartum depression than Canadian-born women. Mainstream psychological approaches to postpartum depression emphasize individual-level risk factors (e.g. hormones, thoughts, emotions) and individualized treatments (e.g. psychotherapy, medication). This conceptualization is problematic when applied to refugee and asylum-seeking women because it fails to acknowledge the migrant experience and the unique set of circumstances from which these women have come. The present theoretical article explores some of the consequences of applying this psychiatric label to the distress experienced by refugee and asylum-seeking women and presents an alternative way of conceptualizing and alleviating this distress.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.080
Threshold uncertainty score0.582

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0200.014
Scholarly communication0.0070.002
Open science0.0020.003
Research integrity0.0030.005
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 head0.038
GPT teacher head0.451
Teacher spread0.413 · 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 designQualitative
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

Citations38
Published2014
Admission routes2
Has abstractyes

Explore more

Same venueHealth An Interdisciplinary Journal for the Social Study of Health Illness and MedicineSame topicMaternal Mental Health During Pregnancy and PostpartumFrench-language works237,207