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Record W3106924358 · doi:10.1177/0008417420968684

Experiences of Intergenerational Trauma in Second-Generation Refugees: Healing Through Occupation

2020· article· en· W3106924358 on OpenAlexvenueaboutno aff
Janany Jeyasundaram, Luisa Yao Dan Cao, Barry Trentham

Bibliographic record

VenueCanadian Journal of Occupational Therapy · 2020
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsnot available
Fundersnot available
KeywordsRefugeeTamilVietnameseNarrativeQualitative researchPsychologyImmigrationGender studiesNarrative inquirySociologyPolitical scienceSocial science

Abstract

fetched live from OpenAlex

BACKGROUND.: Trauma experienced in one generation can affect the health and well-being of subsequent generations, such as impairing life skills, personal contentment, behaviour patterns and sense of self. This phenomenon has predominantly been explored with descendants of European refugees and is not fully understood from an occupational perspective. PURPOSE.: This research explores how intergenerational trauma manifests in the occupational lives of second-generation Ilankai Tamil and Vietnamese refugees. METHODS.: Using qualitative narrative inquiry, 12 adult children of Tamil and Vietnamese refugees residing in the Greater Toronto Area participated in semi-structured interviews. Narratives were thematically analysed. FINDINGS.: Findings illustrate how sociohistorical, cultural and familial contexts influence the way second-generation refugees view what they can and should do. Many healing responses to intergenerational trauma include occupations focused on communal care. IMPLICATIONS.: Findings from this study reveal the unique struggles and needs of two understudied populations and the possibilities for healing through occupation.

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)0.002
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.008
Scholarly communication0.0030.002
Open science0.0010.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.185
GPT teacher head0.418
Teacher spread0.233 · 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

Citations41
Published2020
Admission routes2
Has abstractyes

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Same venueCanadian Journal of Occupational TherapySame topicMigration, Health and TraumaFrench-language works237,207