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Record W2790696120 · doi:10.1177/1363459318763865

“I tend to forget bad things”: Immigrant and refugee young men’s narratives of distress

2018· article· en· W2790696120 on OpenAlexafffundabout
Carla Hilario, John L. Oliffe, Josephine Pui‐Hing Wong, Annette J. Browne, Joy L. Johnson

Bibliographic record

VenueHealth An Interdisciplinary Journal for the Social Study of Health Illness and Medicine · 2018
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsSimon Fraser UniversityToronto Metropolitan UniversityUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsDistressRefugeeNarrativeImmigrationMental healthNarrative inquiryPsychologyGender studiesNorm (philosophy)SociologyDevelopmental psychologyClinical psychologyPsychiatryPolitical science

Abstract

fetched live from OpenAlex

Distress among young immigrant and refugee men has drawn increasing research attention in recent years. Nuanced understandings of distress are needed to inform mental health and public health programming. The purpose of this research was to examine distress from the perspectives of young immigrant and refugee men living in Greater Vancouver, British Columbia, Canada. Thirty-three young men (aged 15-22 years) from diverse immigrant and refugee backgrounds participated in interviews, which were conducted between 2014 and 2015. Data were examined using narrative analysis and theories of masculinities. Three narratives were identified-norming distress, acknowledging distress as ongoing, and situating distress. The findings reveal that the narratives offer different frames through which distress was rendered a norm, or acknowledged and situated in relation to the participants' relationships and to masculine discourses that shaped their expressions of distress. The findings can inform initiatives aimed at providing spaces for diverse young men to acknowledge their distress and to receive support for mental health challenges.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.167
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0080.000
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 head0.041
GPT teacher head0.454
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 teacher head, not a consensus.

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

Citations5
Published2018
Admission routes3
Has abstractyes

Explore more

Same venueHealth An Interdisciplinary Journal for the Social Study of Health Illness and MedicineSame topicMigration, Health and TraumaFrench-language works237,207