COVID-19 Pandemic Experiences of Families in Which a Child/Youth Has Autism and Their Service Providers: Perspectives and Lessons Learned
Bibliographic record
Abstract
PURPOSE: The impacts of the COVID-19 pandemic on autistic children/youth and their families and on service providers are not yet well-understood. This study explored the lived experiences of families with an autistic child and service providers who support them regarding the impacts of the pandemic on service delivery and well-being. METHODS: In this qualitative study, families and service providers (e.g., early intervention staff, service providers, school personnel) supporting autistic children/youth were interviewed. Participants were recruited from a diagnostic site and two service organizations that support autistic children/youth. RESULTS: Thirteen parents and 18 service providers participated in either an individual or group interview. Findings indicate challenges associated with pandemic restrictions and resulting service shifts. These challenges generally imposed negative experiences on the daily lives of autistic children/youth and their families, as well as on service providers. While many were adversely affected by service delivery changes, families and service agencies/providers pivoted and managed challenges. Shifts have had varied impacts, with implications to consider in pandemic planning and post-pandemic recovery. CONCLUSION: Results highlight the need for autism-focused supports, as well as technology and pandemic preparedness capacity building within health, therapeutic and educational sectors in order to better manage shifts in daily routines during emergencies such as a pandemic. Findings also offer instructive consideration in service delivery post-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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".