MétaCan
Menu
Back to cohort
Record W4417296738 · doi:10.1093/pch/pxaf116.076

76 Practices and infrastructure to support patients and families who speak languages other than English (LOE) in Ontario Paediatric Inpatient Units: A multicentre survey study

2025· article· en· W4417296738 on OpenAlexaffabout
Victor Do, Vangie Tsagarakis, Francine Buchanan, Peter W. Gill, Maitreya Coffey, Zia Bismilla, Gita Wahi, Sanjay Mahant

Bibliographic record

VenuePaediatrics & Child Health · 2025
Typearticle
Languageen
FieldHealth Professions
TopicInterpreting and Communication in Healthcare
Canadian institutionsSickKids FoundationHospital for Sick ChildrenMcMaster Children's HospitalUniversity of Alberta
Fundersnot available
KeywordsInterpreterHealth careUnit (ring theory)Descriptive statisticsLimited English proficiencyQualitative propertyPatient safetyLanguage barrier

Abstract

fetched live from OpenAlex

Abstract Background Patients who use languages other than English (LOE) for healthcare communication are at increased risk for adverse events and worse health outcomes in paediatric care settings. Effective communication practices (such as using interpreters) and supports addressing factors such as a sense of belonging and trust are critical to improving health outcomes. We must understand the policies, procedures and practices currently in place in the paediatric inpatient units to support these patients and families. Objectives The objective of this study was to understand the current policies, procedures, processes and infrastructure to support children and families who use LOE for healthcare communication in paediatric inpatient units across Ontario. Design/Methods We conducted a cross-sectional multi-site survey study of paediatric inpatient unit leaders at hospitals in Ontario, Canada. The web-based survey asked multiple choice and open-ended questions about how the unit identifies patient and caregiver language needs, availability and types of interpreter services, frequency and modality of interpreter use on the unit, training on interpreter use provided to staff, tracking of patient safety events involving patients with LOE, and perceived facilitators and barriers to providing care in a patient’s LOE. Quantitative data was analyzed using descriptive statistics, and qualitative data was analyzed using content analysis. Results Responses were received from paediatric inpatient leaders at 25 (83%) of 30 hospitals. Only 11 (44%) of the 25 hospitals had a formal policy for identifying language needs through standardized screening tools or processes. 15 (60%) of the sites surveyed reported that they do not routinely identify interpreter requirements on admission. Less than one-third of the inpatient units had a formal documentation process to ensure that the need for interpreter services was consistently communicated throughout their care. Units report being unable to identify specific health outcome data for patients with LOE as specific data is not collected. Qualitative comments identify challenges including lack of consensus in tools and terminologies and resources for interpreters. There was wide recognition of significant opportunities to better support these patients. Conclusion Despite the recognized importance of interpreters, many paediatric inpatient units lack the foundational processes, procedures and infrastructure to most effectively support patients and families who use LOE. The findings underscore the need for standardized policies and procedures to identify patients and families with LOE so that we effectively support patients. It is important to collect data to help identify and address healthcare disparities for these patients. Our foundational work can help drive ongoing equity-centered design.

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)0.006
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.132
Threshold uncertainty score0.265

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.028
GPT teacher head0.376
Teacher spread0.347 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

Explore more

Same venuePaediatrics & Child HealthSame topicInterpreting and Communication in HealthcareFrench-language works237,207