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Record W4307376531 · doi:10.25071/1920-7336.40935

The Mental Health of Male Sexual Minority Asylum Seekers and Refugees in Nairobi, Kenya: A Aualitative Assessment

2022· article· en· W4307376531 on OpenAlexvenueno aff
Lourence Misedah-Robinson, Vanessa Schick, Michael W. Ross, Solomon Wambua

Bibliographic record

VenueRefuge Canada s Journal on Refuge · 2022
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsnot available
Fundersnot available
KeywordsRefugeeMental healthThematic analysisPsychological interventionPersecutionPsychologyQualitative researchMetropolitan areaPsychiatryMedicinePolitical scienceSociology

Abstract

fetched live from OpenAlex

Very little information exists about the experiences of asylum seekers and refugees who are men who have sex with men (MSM). Therefore, this study explores the psychological distress of MSM asylum seekers and refugees in the Nairobi metropolitan area. We collected data using in-depth interviews transcribed verbatim, coded using NVivo 12 Plus, and analyzed using the six-step thematic analysis framework. Four major themes emerged from the study: psychological distress, traumatic stress symptoms, mental health care access, and coping strategies. Although we did not use any diagnoses, the results indicate that MSM asylum seekers and refugees share mental health problems with other refugees. However, MSM have specific needs that derive from their persecution based on their sexual minority status. The results confirm extant findings, as seen in the discussion, and encourage more research. Further research will inform collaborative, culturally sensitive, and targeted interventions that decrease adverse mental health outcomes for MSM asylum seekers and refugees in the Nairobi metropolitan area.

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)0.002
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 score0.022
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0000.002
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 head0.015
GPT teacher head0.343
Teacher spread0.328 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueRefuge Canada s Journal on RefugeSame topicMigration, Health and TraumaFrench-language works237,207