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Record W3109411594 · doi:10.1177/1049732320970492

Traditional Medicine and Help-Seeking Behaviors for Health Problems Among Somali Bantu Refugees Resettled in the United States

2020· article· en· W3109411594 on OpenAlexaff
Mehret T. Assefa, Rochelle L. Frounfelker, Shanze A. Tahir, Jenna M. Berent, Abdirahman Abdi, Theresa S. Betancourt

Bibliographic record

VenueQualitative Health Research · 2020
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsMcGill University
Fundersnot available
KeywordsSomaliBantu languagesRefugeeEthnic groupHealth careMedicineSociologyPolitical scienceAnthropology

Abstract

fetched live from OpenAlex

Somali refugees have resettled in the United States in large numbers. The focus of this study was specifically on the Somali Bantu refugees, an ethnic minority group from Somalia. The goal of this study was to understand the following: (a) jinn (invisible beings or forces in Islamic theology) and related health problems resulting from jinn possession affecting Somali Bantu refugees, (b) types of traditional healing practices integrated into help-seeking behavior, and (c) pathways of care utilized to address health problems. In total, 20 participant interviews were conducted with Somali Bantu refugees resettled in the United States. Overall, participants described types of jinn and associated health problems. In addition, participants identified different pathways of care, including formal and informal health care. Participants accessed these pathways both concurrently and sequentially. Somali Bantu utilize complex and varied health care services based on their understanding of the causes of health problems and experiences with care providers.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.570
GPT teacher head0.602
Teacher spread0.032 · 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

Citations7
Published2020
Admission routes1
Has abstractyes

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