Exploring Caregiver Learning and Experiences Caring for a Child With Heart Failure: A Qualitative Study
Bibliographic record
Abstract
Background: Paediatric heart failure poses life-long, burdensome symptoms for the health care system and families. Diagnosis and discharge are stressful and anxiety-provoking for caregivers. They face uncertainty about their child's health and become responsible for administering complex care in the home. Little is known about this topic. Our study aimed to explore caregiver learning and experiences caring for a child with heart failure to design and implement a knowledge translation tool. Methods: Qualitative description guided our study. Recruitment occurred in a tertiary cardiac centre in Edmonton, Alberta, Canada. Data collection and analysis occurred concurrently until data redundancy was achieved. Inductive conventional content analysis was used to develop categories. Results: Eleven interviews identified 2 main categories. One relates to how traumatic life experiences impact learning (eg, new diverse ways of learning, stress steepens the learning curve, and learning heart failure takes time). The other relates to families' new life reality after diagnosis (eg, emotional distress and the new reality). Conclusions: This study provides insight into caregivers' learning needs and experiences caring for a child with heart failure. Caregivers describe how the trauma of having their child diagnosed with heart failure negatively impacts their learning capabilities and way of life going forward. Caregiver learning experiences and preferences for digital platforms is also highlighted. This knowledge will inform the design of an online educational tool about pediatric heart failure for caregiver audiences. This tool will empower and improve caregiver decision-making related to their child's daily heart failure management, with the goal to positively impact clincal outcomes, lessen stress and anxiety.
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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 | 0.016 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 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".