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Record W4400168810 · doi:10.1002/eahr.500214

Research with Refugee Populations in North America: Applying the NIH Guiding Principles for Ethical Research

2024· article· en· W4400168810 on OpenAlexaff
Julie M. Aultman, Najah Zaaeed, Colleen Payton, Brittany DiVito, T. J. B. Holland, Jacob Atem

Bibliographic record

VenueEthics & Human Research · 2024
Typearticle
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsDalhousie University
Fundersnot available
KeywordsRefugeePolitical scienceResearch ethicsEngineering ethicsInformed consentEthical issuesMedicineLawAlternative medicineEngineeringPathology

Abstract

fetched live from OpenAlex

This article examines the ethics of research design and the initiation of a study (e.g., recruitment of participants) involving refugee participants. We aim to equip investigators and members of IRBs with a set of ethical considerations and pragmatic recommendations to address challenges in refugee-focused research as it is developed and prepared for IRB review. We discuss challenges including how refugees are being defined and identified; their vulnerabilities before, during, and following resettlement that impacts their research participation; recruitment; consent practices including assent and unaccompanied minors; and conflicts of interest. Ethical guidance and regulatory oversight provided by international bodies, federal governments, and IRBs are important for enforcing the protection of participants. We describe the need for additional ethical guidance and awareness, if not special protections for refugee populations as guided by the National Institutes of Health (NIH) Guiding Principles for Ethical 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)0.275
metaresearch head score (Gemma)0.167
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity
Consensus categoriesMetaresearch, Science and technology studies, Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.477
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.2750.167
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.011
Science and technology studies0.0060.010
Scholarly communication0.0010.000
Open science0.0020.002
Research integrity0.0020.093
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 head0.945
GPT teacher head0.736
Teacher spread0.209 · 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; both teacher heads agree on what is shown here.

Study designTheoretical or conceptual
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

Citations2
Published2024
Admission routes1
Has abstractyes

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