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Record W3017069030 · doi:10.1093/bjsw/bcaa024

Cultural Adaptations of Evidence-Based Mental Health Interventions for Refugees: Implications for Clinical Social Work

2020· article· en· W3017069030 on OpenAlexaff
Maya Fennig

Bibliographic record

VenueThe British Journal of Social Work · 2020
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsMcGill University
Fundersnot available
KeywordsPsychological interventionMental healthRefugeeFidelitySocial workEvidence-based practicePsychologyAdaptation (eye)Intervention (counseling)Public relationsMedicinePsychotherapistPsychiatryPolitical scienceAlternative medicine

Abstract

fetched live from OpenAlex

Abstract As the number of refugees worldwide reaches unprecedented levels, social workers’ ability to provide effective and appropriate mental healthcare to this population is as critical as ever. This article provides a review of contemporary debates revolving around the cultural adaptation (CA) of mental health interventions—when it is warranted, what approach should be taken and what components of an intervention should be adapted. CA is presented as a promising and pragmatic approach to service delivery, one that can assist clinical social workers in designing and implementing interventions that reflect refugees’ local needs and knowledge without neglecting important advances in research evidence and clinical expertise. However, it is not without its challenges. By drawing on literature related to the integration of cultural and contextual factors in mental health interventions and services, the article addresses critical issues in the CA approach and asks: is it possible to strike a balance between fidelity to evidence-based interventions and culturally compatible care?

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.834
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0020.000
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 head0.352
GPT teacher head0.523
Teacher spread0.170 · 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 designNot applicable
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

Citations20
Published2020
Admission routes1
Has abstractyes

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