40 Trust, belonging and understanding - The experiences of families who use languages other than English (LOE) that are hospitalized on general paediatric inpatient units
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,008 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,015 | 0,014 |
| Communication savante | 0,005 | 0,004 |
| Science ouverte | 0,001 | 0,010 |
| Intégrité de la recherche | 0,001 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».