Exploring the experience of family caregivers of children with medical complexity during COVID-19: a qualitative study
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Children with medical complexity have been disproportionately impacted by the COVID-19 pandemic and the associated changes in healthcare delivery. The primary objective of this study was to gain a thorough understanding of the lived experiences of family caregivers of children with medical complexity during the pandemic. METHODS: We conducted semi-structured interviews with family caregivers of children with medical complexity from a tertiary pediatric hospital. Interview questions focused on the aspects of caregiving for children with medical complexity, impact on caregiver mental and physical well-being, changes to daily life secondary to the pandemic, and experiences receiving care in the healthcare system. Interviews were conducted until thematic saturation was achieved. Interviews were audio recorded, deidentified, transcribed verbatim, coded and analyzed using content analysis. RESULTS: Twelve semi-structured interviews were conducted. The interviews revealed three major themes and several associated subthemes: (1) experiences with the healthcare system amid the pandemic (lack of access to healthcare services and increased hospital restrictions, negative clinical interactions and communication breakdowns, virtual care use); (2) common challenges during the pandemic (financial strain, balancing multiple roles, inadequate homecare nursing); and (3) the pandemic's impact on family caregiver well-being (mental toll, physical toll). CONCLUSIONS: Family caregivers of children with medical complexity experienced mental and physical burden due to the intense nature of their caregiving responsibilities that were exacerbated during the pandemic. Our results highlight key priorities for the development of effective interventions to support family caregivers and their children.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".