40 Trust, belonging and understanding - The experiences of families who use languages other than English (LOE) that are hospitalized on general paediatric inpatient units
Bibliographic record
Abstract
Abstract Background Despite recognition that social determinants are critical to health outcomes, many groups, including those with lower incomes, racial/ethnic minorities, and other historically marginalized populations, continue to experience significant social and structural barriers to care. There is need for research that incorporates patient and stakeholder engagement in developing, testing, and disseminating interventions to improve outcomes for patient populations that have been marginalized in the health system. Patients who use a language(s) other than English (LOE) for healthcare communication are at increased risk for experiencing adverse events and worse health outcomes in healthcare settings, including in paediatric hospitals. Canadian census data indicates 12.7% of Canadians predominantly speak a language other than French or English. This work aims to generate knowledge to improve health outcomes for children and families who speak LOE. Objectives (1) To understand the lived experience of families who speak LOE around the hospitalization of their child and (2) To understand the perspectives of patients and families who speak LOE on how the healthcare system can improve care during hospitalization. Design/Methods The premise of this study is participatory research. We designed a qualitative study involving children hospitalized in the General Paediatric Inpatient Unit at the Hospital for Sick Children. The study includes family caregivers of children age 0 to 18 years who identify as speaking LOE. Children and families could speak any language that we were able to obtain interpreter support for. We excluded patients that spoke a traditional Indigenous language as we noted this work required a separate study in partnership with the Indigenous community. We conducted semi-structured virtual individual interviews with children and families post-discharge with a medical interpreter. An interview guide was developed and transcripts were analyzed using thematic analysis. We took several steps to ensure an effective study design given our goals of participatory research including ensuring patient and family engagement throughout the design process. Results 19 families participated in the interviews and 16 different languages were represented amongst our participant groups. Our study is unique in especially unique in it's ability to recruit and interview individuals with so many different primary languages. Our study provides rich insight into opportunities within the paediatric inpatient setting for quality improvement to address the health disparities experienced by patients and families who use LOE for healthcare communication. Our analysis of the interviews identify several themes that provide insight into the lived experience of patients and families who use LOE for healthcare communication. 1) Inconsistent interpreter use affects communication “in the moment” and the ability to develop an “overall understanding” of the current clinical situation and broader healthcare context. Our participants note a "snowball effect" when interpreters are not consistently utilized. 2) In addition to language, multiple additional social factors contribute to an increased sense of vulnerability and modulates how families “trust” the healthcare system. 3) Despite families expressing positive experiences, they noted that they did not, nor expect to feel like they “belonged” within the healthcare system. It is critical to recognize the complex interplay between many factors surrounding our participants’ identities. Though they use a LOE for healthcare, the intersectional marginalization they experience was thoroughly represented during our conversations Conclusion Our research aimed to take a participatory approach to consider the lived experience of patients and families who use LOE for healthcare communication. This work builds on the current literature on patient and family centered care, experiences of families who speak LOE and participatory community-based research in the paediatric hospital settings. Our study's findings support the development of a research and intervention agenda in Canada to improve health outcomes for patients and families with LOE.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.014 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".