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Record W2889201314 · doi:10.1177/0020872818796147

Challenges of developing and conducting an international study of resilience in migrant adolescents

2018· article· en· W2889201314 on OpenAlexaff
Kristin Hadfield, Michael Ungar, Alan Emond, Kim Foster, Justine M. Gatt, Amanda J. Mason‐Jones, Steve Reid, Linda Theron, Trecia A. Wouldes, Qiaobing Wu

Bibliographic record

VenueInternational Social Work · 2018
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsDalhousie University
FundersNational Health and Medical Research CouncilUniversity of AucklandNational Research FoundationUniversity of BristolUniversity of York
KeywordsVulnerability (computing)Mental healthPsychological resilienceWork (physics)Resilience (materials science)Sample (material)PsychologySociologyPolitical sciencePublic relationsEconomic growthSocial psychologyPsychiatry

Abstract

fetched live from OpenAlex

The sequelae of migration and the effects of local migration policies on children’s physical and mental health are critical to examine, particularly given the historically high numbers of migrants and displaced people. The vulnerability of the study sample and the need to work across cultures and contexts makes research on this group challenging. We outline lessons learned through conducting a pilot study of resilience resources and mental health among migrant youth in six countries. We describe the benefits and challenges, and then provide recommendations and practical advice for social work researchers attempting cross-cultural team research on migrants.

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)0.202
metaresearch head score (Gemma)0.112
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.798
Threshold uncertainty score0.984

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2020.112
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0090.006
Scholarly communication0.0090.009
Open science0.0030.012
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0040.001

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 head0.124
GPT teacher head0.421
Teacher spread0.297 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designQualitative
DomainMethods
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

Citations3
Published2018
Admission routes1
Has abstractyes

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