Navigating healthcare during a pandemic: what parents of CHD children want healthcare professionals to know
Bibliographic record
Abstract
BACKGROUND: The COVID pandemic has had deleterious effects on the mental health of the global population. Parents of children with CHD were particularly vulnerable to negative mental health outcomes such as depression, anxiety, and perceived stress. A better understanding of the CHD parent experiences, needs, and concerns while navigating the healthcare system during a pandemic is needed. METHODS: Online survey responses from 71 parents of young children with CHD representing families across the United States of America and Canada were analysed. Qualitative data were collected one year into the COVID pandemic. Thematic analysis was used to examine responses to the open-ended question "What would you like healthcare professionals (doctors, nurses) to know about your experience of being a parent with a child with CHD during the COVID-19 pandemic?." RESULTS: Two major themes with subthemes and an umbrella theme emerged from the parents' responses (1) Pandemic Parenting: The Emotional Toll of Hospital Visitation Restrictions, Dealing with Social Distancing, Feeling Isolated, Decision Making in Uncertainty, and Playing it Safe versus Returning to Normal and (2) Unmet Expectations of Care: Needing Information, Wanting Empathy, Requesting Respect, Questioning Care Quality, and the umbrella theme of: Our Lives were Turned Upside Down. CONCLUSION: CHD parents describe a negative impact of healthcare-related challenges during the COVID pandemic. These findings may offer insight to how healthcare professionals can better support the mental health and care burden of CHD parents during future pandemics.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".