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Record W4388849373 · doi:10.1177/08445621231215845

Experiences of Providers and Immigrants/Refugees with Health Care: A Meta-Synthesis of the Latin American Context

2023· review· en· W4388849373 on OpenAlexvenueno aff
Mayckel da Silva Barreto, Isadora Wolf, Nathalie Campana de Souza, Lorena Franco Buzzerio, Viviane Cazetta de Lima Vieira, Maria do Céu Figueiredo-Barbieri, Sônia Silva Marcon

Bibliographic record

VenueCanadian Journal of Nursing Research · 2023
Typereview
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsnot available
Fundersnot available
KeywordsRefugeeImmigrationLatin AmericansContext (archaeology)Health carePolitical scienceEconomic growthEconomicsGeography

Abstract

fetched live from OpenAlex

INTRODUCTION: The experiences of providers and immigrants/refugees related to healthcare in the Latin American context have not yet been aggregated. This study aimed to synthesize the qualitative evidence on this theme. METHOD: A systematic review of qualitative evidence with meta-synthesis. After identification, eligible studies were evaluated for methodological quality, and information was systematically analyzed. RESULTS: The sample comprised 26 articles. The meta-theme shows that the experiences of providers and immigrants/refugees are determined by multilevel factors. In a macro-context, these factors involve the vulnerabilities of immigrants/refugees and the healthcare system/model, and in a closer context, they involve the lack of professional training in cultural skills and communication; language barriers; and prejudice/xenophobia. Within healthcare, the relationship is mostly conflictual, asymmetric, and unable to solve problems, leading to negative repercussions for both. CONCLUSIONS: Managers involved in developing public policies and providers must consider improving the interrelationship between healthcare services and the migrant population.

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)0.002
metaresearch head score (Gemma)0.000
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: Review · Consensus signal: Review
Teacher disagreement score0.817
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.247
GPT teacher head0.489
Teacher spread0.243 · 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
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

Citations8
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Nursing ResearchSame topicMigration, Health and TraumaFrench-language works237,207