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Record W4387508475 · doi:10.7202/1106277ar

Expérience des réfugiés, demandeurs d’asile et migrants sans statut et offre de services de santé et sociaux pendant la pandémie au Québec

2022· article· fr· W4387508475 on OpenAlexaffvenueabout
Lara Gautier, Naïma Bentayeb

Bibliographic record

VenueAlterstice Revue internationale de la recherche interculturelle · 2022
Typearticle
Languagefr
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsMcGill UniversityÉcole Nationale d'Administration PubliqueUniversité de MontréalCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal
Fundersnot available
KeywordsHumanitiesCoronavirus disease 2019 (COVID-19)Political science2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)ArtMedicineVirology

Abstract

fetched live from OpenAlex

À l’échelle mondiale, la pandémie de la COVID-19 a affecté de manière disproportionnée les migrants en termes de risque d’infection, de santé mentale, de processus d’immigration et d’accès aux services de santé et sociaux et aux ressources de soutien. Face à la COVID-19, les organismes communautaires et les établissements du réseau public de services de santé et services sociaux du Québec ont réagi en adaptant leurs pratiques et en offrant davantage de services à distance. Ils ont joué un rôle central dans la prévention et le traitement des infections à la COVID-19 et ils ont oeuvré en matière de protection sociale. Ce numéro thématique offre une réflexion approfondie des chercheurs de l’Institut universitaire SHERPA de Montréal sur les expériences des migrants pendant la pandémie de COVID-19 au Québec et sur la valorisation d’innovations dans les services communautaires et institutionnels mises en oeuvre.

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)0.002
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.178
Threshold uncertainty score0.358

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.005
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.111
GPT teacher head0.423
Teacher spread0.312 · 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

Citations2
Published2022
Admission routes3
Has abstractyes

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Same venueAlterstice Revue internationale de la recherche interculturelleSame topicMigration, Health and TraumaFrench-language works237,207