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Record W4407558153 · doi:10.1177/17504589251316744

Language-specific approaches to reduce perioperative stress and anxiety related to anaesthesia for patients with limited English proficiency: A narrative review

2025· review· en· W4407558153 on OpenAlexafffund
Kanwarpreet Kaur Dhaliwal, Nitasha Puri, Amolpreet S. Toor

Bibliographic record

VenueJournal of Perioperative Practice · 2025
Typereview
Languageen
FieldHealth Professions
TopicInterpreting and Communication in Healthcare
Canadian institutionsFraser HealthUniversity of British Columbia
FundersFraser Health AuthorityFaculty of Medicine, University of British Columbia
KeywordsAnxietyPerioperativeMedicineFirst languageEnglish languageMEDLINEIntensive care medicineClinical psychologyPsychologyAnesthesiaPsychiatryPathology

Abstract

fetched live from OpenAlex

Many patients experience perioperative anxiety due to a variety of different reasons. Essential processes of shared decision-making and informed consent may help to mitigate anxiety, yet language barriers may hinder this particularly in diverse patient populations. As such, language-specific approaches in anaesthesia care play a crucial role in reducing perioperative stress and anxiety among patients with limited English proficiency. This review examines which methods during anaesthetic assessments and shared decision-making processes enable anaesthetists to communicate effectively with patients who have limited English proficiency and thereby reduce perioperative stress. Findings suggest that collaborating with patients in their native language significantly reduces anxiety and improves understanding, while transcreation - culturally adapted translation - enhances the effectiveness of communication. To decrease perioperative anxiety among populations with limited English proficiency and improve surgical outcomes, it is important to enhance anaesthesia-focused training for interpreters, increase diversity in the anaesthesia field, and develop culturally relevant patient education materials.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.746
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0000.000

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 head0.455
Teacher spread0.344 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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
Published2025
Admission routes2
Has abstractyes

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