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
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".