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Record W3149329785 · doi:10.7202/1082534ar

Précarité et accès aux soins de physiothérapie des migrants sans statut de séjour légal à Genève

2020· article· fr· W3149329785 on OpenAlexvenueno aff
Théogène Octave Gakuba, Jean-Luc Rossier, Mélinée Schindler

Bibliographic record

VenueAlterstice Revue internationale de la recherche interculturelle · 2020
Typearticle
Languagefr
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical scienceArt

Abstract

fetched live from OpenAlex

La Suisse, à l’instar d’autres pays, est la destination de migrants qui arrivent pour différentes raisons : économiques, politiques, professionnelles, études, regroupement familial… Parmi eux, un certain nombre sont considérés comme des « sans-papiers » : ils séjournent en Suisse sans documents de séjour valables du point de vue du droit des étrangers. Ces migrants sans papiers sont confrontés à des conditions de vie difficiles et, ne disposant pas d’assurance maladie, éprouvent des difficultés d’accès aux soins de santé.Cet article se base sur les résultats d’une recherche que nous avons menée auprès de patients migrants sans statut de séjour légal en consultation de physiothérapie à la Haute école de santé de Genève (HEdS-Genève). Plusieurs ont vécu des événements difficiles dans leur pays d’origine et de transit (traumatismes de guerre, violences…) ou en vivent dans leur pays d’accueil (problèmes de statut de séjour, séparations de leurs familles, chômage, problème de logement, etc.) et, souvent, lors des consultations, ont des demandes d’aide psychosociale qui dépassent le cadre physiothérapeutique. Dans cet article, nous abordons les enjeux psychosociaux et interculturels de leur prise en charge physiothérapeutique par les étudiants stagiaires de la HEdS-Genève.

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)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score0.099

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.214
GPT teacher head0.438
Teacher spread0.224 · 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 designQualitative
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

Citations0
Published2020
Admission routes1
Has abstractyes

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