Mental health challenges during COVID-19: perspectives from parents with children with neurodevelopmental disabilities
Bibliographic record
Abstract
BACKGROUND: The global pandemic and subsequent denials, delays, and disruptions in essential daily activities created significant challenges for children with neurodevelopmental disorders (NDDs) and their parents. Public health restrictions during the COVID-19 pandemic limited access to supports and services required by children with NDDs to maintain their health and well-being. OBJECTIVE: This study sought to understand the impacts of these public health measures and restrictions on mental health from the perspective of parents with children with NDDs to inform pathways for public health policies responsive to the needs of this population. METHOD: Interpretive descriptive design was used to guide data collection and data analysis. Forty caregivers were interviewed about their experience with pandemic restrictions. FINDINGS: Generic policy measures contributed to many gaps in families' social support systems and contributed to mental health challenges for children and their parents. Four themes emerged: 1) lack of social networks and activities, 2) lack of access to health and social supports, 3) tension in the family unit, and 4) impact on mental health for children and their parents. RECOMMENDATIONS: Emergency preparedness planning requires a disability inclusive approach allocating resources for family supports in the home and community. Families identified supports to minimize further pandemic disruptions and enhance recovery.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| 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".