Back

A Qualitative Descriptive Study Exploring Caregivers' Information Needs and Experience Caring for a Child with Chronic Heart Failure

Cunningham, C.; Conway, J.; Zahoui, Z.; Haykowsky, M. J.; Scott, S. D.

2024-02-29 cardiovascular medicine
10.1101/2024.02.27.24303476 medRxiv
Show abstract

BackgroundChronic phenotypes of pediatric heart failure pose life-long burdensome symptoms for the healthcare system and families. Treatment involves complex medical therapies with few surgical options until more advanced, refractory stages. Caregivers must become proficient in providing care to these vulnerable children in the home environment, which imposes a high amount of stress. Despite caregiver demands, little is known about caregiver information needs and experiences caring for a child with chronic heart failure. Therefore, a qualitative approach employing semi-structured interviews aimed to fill this knowledge gap. Methods and ResultsA qualitative descriptive methodology guided our study. Participants were recruited from a tertiary cardiac centre in Edmonton, Alberta, Canada. Data collection and analysis occurred concurrently. Semi-structured interviews were conducted until data redundancy was achieved. Inductive content analysis was used to uncover categories. Eleven interviews identified five main categories. Three categories related to information needs: 1) sources of information, 2) profound stress steepens the learning curve, and 3) acknowledging that learning heart failure takes time. Two categories related to experience: 4) the emotional rollercoaster, feelings of emotional distress, and 5) the hard reality of caring for a child with heart failure: always on the clock. ConclusionsTo our knowledge, this is the first North American situated qualitative study to provide insights about caregivers information needs and experiences caring for a child with chronic heart failure. This knowledge will enhance future information provision, optimizing clinical care and outcomes.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.