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Record W4385843468 · doi:10.15273/hpj.v3i2.11596

More Research is Needed to Understand the Impact of Language Discordance in Long-Term Care in Canada

2023· article· en· W4385843468 on OpenAlexaffabout
Alixe Ménard, Mary Scott, Annie H. Sun, Anna Cooper-Reed, Prabasha Rasaputra, Amy T. Hsu

Bibliographic record

VenueHealthy Populations Journal · 2023
Typearticle
Languageen
FieldHealth Professions
TopicInterpreting and Communication in Healthcare
Canadian institutionsOttawa HospitalBruyèreUniversity of Ottawa
Fundersnot available
KeywordsHealth careLanguage barrierTerm (time)NursingMedicineQuality (philosophy)Long-term carePsychologyGerontologyPolitical science

Abstract

fetched live from OpenAlex

There is consistent evidence highlighting the risks of language barriers and discordance to quality care and patient safety, especially in primary care and hospital settings. However, there has been limited research on the impact of language barriers and discordance on quality care for older individuals residing in long-term care. In this commentary, we highlight select studies on differences in health care access and outcomes that linguistic minorities experience in Canadian long-term care homes, and discuss the importance of tackling language barriers and discordance to equitable long-term care. This article reflects on the impact of language discordance in health care, an identified determinant of health disparities, and calls for further research on health inequity experienced by older adults in Canada as well as strategies toward more equitable care.

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)0.011
metaresearch head score (Gemma)0.044
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.893
Threshold uncertainty score0.773

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.044
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.003
Science and technology studies0.0130.005
Scholarly communication0.0060.003
Open science0.0030.004
Research integrity0.0020.005
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 head0.261
GPT teacher head0.593
Teacher spread0.332 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations4
Published2023
Admission routes2
Has abstractyes

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