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Record W2415115206 · doi:10.1371/journal.pone.0146798

Knowledge of Healthcare Coverage for Refugee Claimants: Results from a Survey of Health Service Providers in Montreal

2016· article· en· W2415115206 on OpenAlexafffundabout
Mónica Ruiz‐Casares, Janet Cleveland, Youssef Oulhote, Catherine Dunkley-Hickin, Cécile Rousseau

Bibliographic record

VenuePLoS ONE · 2016
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsJewish General HospitalCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalMcGill University
FundersCanadian Institutes of Health Research
KeywordsRefugeeInterimHealth careService providerMedicineConfusionFamily medicineNursingService (business)PsychologyBusinessPolitical science

Abstract

fetched live from OpenAlex

Following changes to the Interim Federal Health (IFH) program in Canada in 2012, this study investigates health service providers' knowledge of the healthcare coverage for refugee claimants living in Quebec. An online questionnaire was completed by 1,772 staff and physicians from five hospitals and two primary care centres in Montreal. Low levels of knowledge and significant associations between knowledge and occupational group, age, and contact with refugees were documented. Social workers, respondents aged 40-49 years, and those who reported previous contact with refugee claimants seeking healthcare were significantly more likely to have 2 or more correct responses. Rapid and multiple changes to the complex IFH policy have generated a high level of confusion among healthcare providers. Simplification of the system and a knowledge transfer strategy aimed at improving healthcare delivery for IFH patients are urgently needed, proposing easy avenues to access rapidly updated information and emphasizing ethical and clinical issues.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.116
GPT teacher head0.353
Teacher spread0.236 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations35
Published2016
Admission routes3
Has abstractyes

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Same venuePLoS ONESame topicMigration, Health and TraumaFrench-language works237,207