A Qualitative Descriptive Study Exploring Caregivers' Information Needs and Experience Caring for a Child with Chronic Heart Failure
Bibliographic record
Abstract
Background: Chronic 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: A 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. Results: 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. Conclusions: To our knowledge, this is the first North American situated qualitative study to provide key insights about caregivers? information needs and experiences caring for a child with chronic heart failure. This knowledge provides insight to enhance and optimize clinical care and outcomes in this population and will inform the design of an arts-based information tool targeting caregivers.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".