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Record W4391757293 · doi:10.32920/25209338.v1

An Institutional Ethnographic Analysis of the Organization of French Healthcare and Disability Services for Francophones in Canada

2024· preprint· en· W4391757293 on OpenAlexaffabout
Katherine MacEachern

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldHealth Professions
TopicInterpreting and Communication in Healthcare
Canadian institutionsToronto Metropolitan UniversityEducation and Early Childhood DevelopmentUniversity of Ottawa
Fundersnot available
KeywordsEthnographyFrenchHealth careHealthcare systemService (business)Healthcare serviceWork (physics)SociologyPolitical scienceNursingPublic relationsLinguisticsMedicineBusinessAnthropology

Abstract

fetched live from OpenAlex

This Institutional Ethnographic study analyzes how the Canadian healthcare systems in the provinces of Manitoba, Ontario, and the Northwest Territories are organized around the French language. Eight Francophone parents of disabled children were interviewed from 2015 onwards as part of the Inclusive Early Childhood Service System Project. The participants’ experiences describe how the healthcare systems force minority-language speakers to conform to the monolingual Anglophone systems by making them speak English and having limited availability of French services and health information. Additionally, the data detailed the work that French-speaking families do to use services in their chosen language, such as being advocates for their child, travelling to French services, and navigating the healthcare system with little help from healthcare professionals. The findings demonstrate that the Canadian provincial and territorial healthcare systems are organized in a monolingual way and more needs to be done to help all minority-language speakers in Canada.

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)0.003
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.090
Threshold uncertainty score0.653

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0180.007
Scholarly communication0.0050.001
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.035
GPT teacher head0.396
Teacher spread0.361 · 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
Published2024
Admission routes2
Has abstractyes

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Same topicInterpreting and Communication in HealthcareFrench-language works237,207