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Record W2582298370 · doi:10.3390/ijerph14020125

Recruitment of Refugees for Health Research: A Qualitative Study to Add Refugees’ Perspectives

2017· article· en· W2582298370 on OpenAlexafffundabout
Patricia Gabriel, Janusz Kaczorowski, Nicole S. Berry

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2017
Typearticle
Languageen
FieldMedicine
TopicGlobal Health and Surgery
Canadian institutionsSimon Fraser UniversityUniversité de MontréalUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsRefugeeQualitative researchGerontologyPsychologySociologyMedicinePolitical scienceSocial science

Abstract

fetched live from OpenAlex

Research is needed to understand refugees' health challenges and barriers to accessing health services during settlement. However, there are practical and ethical challenges for engaging refugees as participants. Despite this, there have been no studies to date specifically investigating refugee perspectives on factors affecting engagement in health research. Language-concordant focus groups in British Columbia, Canada, with four government-assisted refugee language groups (Farsi/Dari, Somali, Karen, Arabic) inquired about willingness to participate in health research. Twenty-three variables associated with the willingness of refugees to participate in health research were elicited. Variables related to research design included recruitment strategies, characteristics of the research team members and the nature of the research. Variables related to individual participants included demographic features such as gender and education, attitudes towards research and previous experience with research. This research can be used to increase opportunities for refugees' engagement in research and includes recommendations for subgroups of refugees that may have more difficulties engaging in research.

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)0.025
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.453
Threshold uncertainty score0.874

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0250.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.424
GPT teacher head0.611
Teacher spread0.187 · 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.

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

Citations42
Published2017
Admission routes3
Has abstractyes

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