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Record W3142667798 · doi:10.1177/13634615211002690

Refugee mental health and human rights: A challenge for global mental health

2021· editorial· en· W3142667798 on OpenAlexaff
Rachel Kronick, G. Eric Jarvis, Laurence J. Kirmayer

Bibliographic record

VenueTranscultural Psychiatry · 2021
Typeeditorial
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsMcGill University
Fundersnot available
KeywordsRefugeeMental healthForced migrationGlobal mental healthHuman rightsParticipatory action researchPolitical scienceThematic analysisSociologyPsychologyQualitative researchSocial sciencePsychiatryLaw

Abstract

fetched live from OpenAlex

that presents recent work that deepens our understanding of the refugee experience-from the forces of displacement, through the trajectory of migration, to the challenges of resettlement. Mental health research on refugees and asylum seekers has burgeoned over the past two decades with epidemiological studies, accounts of the lived experience, new conceptual frameworks, and advances in understanding of effective treatment and intervention. However, there are substantial gaps in available research, and important ethical and methodological challenges. These include: the need to adopt decolonizing, participatory methods that amplify refugee voices; the further development of frameworks for studying the broad impacts of forced migration that go beyond posttraumatic stress disorder; and more translational research informed by longitudinal studies of the course of refugee adaptation. Keeping a human rights advocacy perspective front and center will allow researchers to work in collaborative ways with both refugee communities and receiving societies to develop innovative mental health policy and practice to meet the urgent need for a global response to the challenge of forced migration, which is likely to grow dramatically in the coming years as a result of the impacts of climate change.

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 categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.105
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.020
GPT teacher head0.374
Teacher spread0.354 · 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
GenreEditorial

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

Citations51
Published2021
Admission routes1
Has abstractyes

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