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Record W2981699589 · doi:10.1017/dmp.2019.90

Literature Review: Strategies for Addressing Language Barriers During Humanitarian Relief Operations

2019· review· en· W2981699589 on OpenAlexaff
Carlo Riccardo Rossi, Sylvain Grenier, Régis Vaillancourt

Bibliographic record

VenueDisaster Medicine and Public Health Preparedness · 2019
Typereview
Languageen
FieldHealth Professions
TopicInterpreting and Communication in Healthcare
Canadian institutionsChildren's Hospital of Eastern OntarioCanadian Blood ServicesUniversity of OttawaCanadian Armed Forces
Fundersnot available
KeywordsContext (archaeology)Nexus (standard)Language barrierNorth Atlantic TreatyInterpreterDiplomacyPublic relationsPolitical scienceMedical educationMedicineKnowledge managementPsychologyEngineeringComputer scienceLawGeography

Abstract

fetched live from OpenAlex

Humanitarian relief operations (HUMRO) represent a nexus between military diplomacy and global health engagement, and may play an increasing role in military operations in the near future. Language barriers between providers and the individuals being assisted are a significant constraint on HUMRO. A literature review was conducted to identify recommendations to address patient-provider language discordance in the international HUMRO context. This was supplemented by a North Atlantic Treaty Organization and US Department of Defense doctrinal review to identify existing best practices for addressing language barriers. Four general themes were identified: (1) print-based aids, (2) information technology, (3) bilingual responders, and (4) the effective use of medical interpreters in the HUMRO setting. Each strategy is reviewed. Informed by expert opinion, we provide concrete leadership and training recommendations for how HUMRO providers might more effectively communicate with patients in a deployed language-discordant context.

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)0.003
metaresearch head score (Gemma)0.002
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: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.547
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.001
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
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 head0.235
GPT teacher head0.526
Teacher spread0.291 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations7
Published2019
Admission routes1
Has abstractyes

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