Impact of COVID-19 pandemic in children using non-invasive ventilation: a thematic analysis of caregivers answers to a survey study
Bibliographic record
Abstract
Purpose: The COVID-19 pandemic has resulted in drastic changes in people's lives, more so in individuals with chronic conditions, such as children with chronic respiratory disorders requiring home non-invasive ventilation. Our research question was: How has the COVID-19 pandemic affected the daily lives of children using home NIV and their families and their NIV adherence? Methods: An anonymous online survey was administered to caregivers of pediatric patients using home NIV followed at the Stollery's Pediatric NIV Program in Alberta, Canada, between September 2020 and September 2021. Thematic analysis was conducted for the identification of emerging themes. Results/findings: Four themes were identified: (1) positive effects, (2) negative effects, (3) neutral effects, and (4) impact on NIV adherence. Effects of COVID-19 on children and families were reported by 55 respondents (57% response rate). Positive effects included a slower lifestyle, more family time, and less recurrent acute respiratory illness. Negative effects included increased parental anxiety, prolonged social isolation beyond imposed restrictions, and limited access to health supplies. Despite these negative effects, 90% of respondents reported adequate maintenance or even increases in their child's NIV use. A general sense of benefit in the virtual specialized care model was also highlighted. Conclusion: COVID-19 resulted in varying levels of impact on the lives of children using NIV, not unlike the general population. Negative effects, however, appeared to intensify in these technology-dependent children. NIV adherence, however, was prioritized by families and even increased during COVID-19. Further research is needed to analyse the potential benefits of virtual models of specialized care.
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".