One size does not fit all: a qualitative exploration of the experiences of Canadian youth with neurodevelopmental disabilities during the COVID-19 pandemic
Bibliographic record
Abstract
Introduction: Youth with neurodevelopmental disabilities (NDD) were disproportionately impacted by the COVID-19 pandemic due to health and socioeconomic factors and system level disruption of essential supports. To date, few studies have engaged directly with youth with NDD to understand how they were been impacted by the pandemic. The aim of this study was to uncover experiences of youth with NDD during the COVID-19 pandemic. Methods: Purposive sampling was used to recruit Canadian youth (age 18-30, inclusive) with NDD. Participants were provided with the option of participating in a written (online) or verbal (Zoom) interview. Deductive coding and inductive analysis were used to develop themes. Results: Forty youth participated in an interview. We discuss the impacts of the COVID-19 pandemic on participants in five key areas: education and academic performance, access to disability and healthcare services, social connectedness, participation in society, and mental health. Within these areas, both positive and negative experiences were reported. A secondary finding emerged related to the impact of gender identity on access to services. Discussion: Our study highlights the need for policy approaches that are flexible and responsive to the variability of needs among Canadian youth with NDD moving forward.
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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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.029 | 0.016 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.008 |
| 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".