Key learnings from <scp>COVID</scp>‐19 to sustain quality of life for families of individuals with <scp>IDD</scp>
Bibliographic record
Abstract
COVID-19 has very publicly had profound impacts on the health system of every country in the world. Over 4.5 million people have lost their lives. School closures worldwide where up to 1.6 billion of the world's children have been out of school, are also prominent in world news. Behind these public impacts are the families. In this paper, we focus on the experiences of families with people with intellectual and developmental disabilities (IDD) through analysis of two data sets: the emerging research literature and contributions from our author team who have lived experience of intellectual and developmental disability in the context of COVID-19. From these two data sets, we discern five themes of the impact of the pandemic: on health, on education, on services and supports, on families and finally on relationships beyond the family. We conclude with lessons from those living with intellectual and developmental disabilities, the carers and the individuals themselves to draw implications for supporting families in the context of disability during future pandemics.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.031 | 0.003 |
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".