Impact of the Covid-19 Pandemic on Children and Families in PICU Follow-Up Clinic
Bibliographic record
Abstract
Abstract The novel coronavirus disease 2019 (COVID-19) pandemic disrupted the lives of many families, especially those of children with chronic health problems. Little is known about the impact of this pandemic on the health and well-being of critically ill children and their families after their discharge from pediatric intensive care unit (PICU) hospitalization. This study describes the repercussions of the COVID-19-related lockdown on the physical and psychological wellbeing, quality of life, and access to resources of PICU survivors and their families. This was a prospective cohort study of children and families followed at the Centre Hospitalier Universitaire Ste-Justine PICU follow-up clinic from October 2018 to February 2020. Families were contacted by phone to complete validated questionnaires (Pediatric Quality of Life Inventory, Hospital Anxiety and Depression Scale) and to evaluate the impact of the COVID-19 pandemic on their access to medical care and extrafamilial support. Fifty-five families were contacted between November and December 2020. Quality of life scores were 88.1 ± 16.9 and 83.8 ± 13.9 for physical and psychosocial aspects, respectively. Symptoms of anxiety and depression were detected in 23.6 and 3.6% of respondent parents, respectively. A significant proportion of families reported canceled or delayed health care appointments (65.5%) and difficulty with medication access (12.7%). Twenty-five families (45.5%) reported a significant decrease in income. We could not identify any statistically significant predictors for lower quality of life scores. Difficulty accessing medical care was associated with higher symptoms of anxiety and/or depression in parents on multivariate analysis (p = 0.02). In conclusion, the COVID-19 pandemic has had a significant negative impact on PICU survivors' access to medical resources and extrafamilial support.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".