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Sociodemographic Characteristics and Mental and Physical Health Diagnoses of Yazidi Refugees Who Survived the Daesh Genocide and Resettled in Canada

2023· article· en· W4383998663 on OpenAlexaffabout
Nour Hassan, Annalee Coakley, Ibrahim Al Masri, Rachel Talavlikar, Michael Aucoin, Rabina Grewal, Adl K. Khalaf, S. M. Woahid Murad, Kerry McBrien, Paul E. Ronksley, Gabriel E. Fabreau

Bibliographic record

VenueJAMA Network Open · 2023
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsLibin Cardiovascular Institute of AlbertaUniversity of Calgary
Fundersnot available
KeywordsRefugeeMental healthGenocideMedicineMedical diagnosisPsychiatryPoison controlSuicide preventionEnvironmental healthLawPolitical science

Abstract

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Importance: The health status of Yazidi refugees, a group of ethnoreligious minority individuals from northern Iraq who resettled in Canada between 2017 and 2018 after experiencing genocide, displacement, and enslavement by the Islamic State (Daesh), is unknown but important to guide health care and future resettlement planning for Yazidi refugees and other genocide victims. In addition, resettled Yazidi refugees requested documentation of the health impacts of the Daesh genocide. Objective: To characterize sociodemographic characteristics, mental and physical health conditions, and family separations among Yazidi refugees who resettled in Canada. Design, Setting, and Participants: This retrospective clinician- and community-engaged cross-sectional study included 242 Yazidi refugees seen at a Canadian refugee clinic between February 24, 2017, and August 24, 2018. Sociodemographic and clinical diagnoses were extracted through review of electronic medical records. Two reviewers independently categorized patients' diagnoses by International Statistical Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes and ICD-10-CM chapter groups. Diagnosis frequencies were calculated and stratified by age group and sex. Five expert refugee clinicians used a modified Delphi approach to identify diagnoses likely to be associated with Daesh exposure, then corroborated these findings with Yazidi leader coinvestigators. A total of 12 patients without identified diagnoses during the study period were excluded from the analysis of health conditions. Data were analyzed from September 1, 2019, to November 30, 2022. Main Outcomes and Measures: Sociodemographic characteristics; exposure to Daesh captivity, torture, or violence (hereinafter, Daesh exposure); mental and physical health diagnoses; and family separations. Results: Among 242 Yazidi refugees, the median (IQR) age was 19.5 (10.0-30.0) years, and 141 (58.3%) were female. A total of 124 refugees (51.2%) had direct Daesh exposure, and 60 of 63 families (95.2%) experienced family separations after resettlement. Among 230 refugees included in the health conditions analysis, the most common clinical diagnoses were abdominal and pelvic pain (47 patients [20.4%]), iron deficiency (43 patients [18.7%]), anemia (36 patients [15.7%]), and posttraumatic stress disorder (33 patients [14.3%]). Frequently identified ICD-10-CM chapters were symptoms and signs (113 patients [49.1%]), nutritional diseases (86 patients [37.4%]), mental and behavioral disorders (77 patients [33.5%]), and infectious and parasitic diseases (72 patients [31.3%]). Clinicians identified mental health conditions (74 patients [32.2%]), suspected somatoform disorders (111 patients [48.3%]), and sexual and physical violence (26 patients [11.3%]) as likely to be associated with Daesh exposure. Conclusions and Relevance: In this cross-sectional study, Yazidi refugees who resettled in Canada after surviving the Daesh genocide experienced substantial trauma, complex mental and physical health conditions, and nearly universal family separations. These findings highlight the need for comprehensive health care, community engagement, and family reunification and may inform care for other refugees and genocide victims.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.284
Threshold uncertainty score0.411

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.333
Teacher spread0.309 · 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 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".

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Citations9
Published2023
Admission routes2
Has abstractyes

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