Mental health impacts of the COVID-19 pandemic on children with underlying health and disability issues, and their families and health care providers
Bibliographic record
Abstract
Objectives: The COVID-19 pandemic has impacted mental health at a population level. Families of children with health vulnerabilities have been disproportionately affected by pandemic-related policies and service disruptions as they substantially rely on the health and social care system. We elicited the impact of the COVID-19 pandemic on children with health and disability-related vulnerabilities, their families, and their health care providers (HCPs). Methods: Children with diverse health vulnerabilities (cardiac transplantation, respiratory conditions, sickle cell disease, autism spectrum disorder, mental health issues, and nearing the end of life due to a range of underlying causes), as well as their parents and HCPs, participated in semi-structured interviews. Data were analyzed using qualitative content analysis in determining themes related to impact and recommendations for practice improvement. Results: A total of 262 participants (30 children, 76 parents, 156 HCPs) were interviewed. Children described loneliness and isolation; parents described feeling burnt out; and HCPs described strain and a sense of moral distress. Themes reflected mental health impacts on children, families, and HCPs, with insufficient resources to support mental health; organizational and policy influences that shaped service delivery; and recommendations to enhance service delivery. Conclusion: Children with health vulnerabilities, their families and HCPs incurred profound mental health impacts due to pandemic-imposed public health restrictions and care shifts. Recommendations include the development and application of targeted pandemic information and mental health supports. These findings amplify the need for capacity building, including proactive strategies and mitigative planning in the event of a future pandemic.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".