Left behind in the “Return to Normal”: People with intellectual and developmental disabilities' outcomes and supports 4 years into COVID‐19
Bibliographic record
Abstract
Abstract The COVID‐19 pandemic has impacted people with intellectual and developmental disabilities (IDD) immensely. While the pandemic and life during it has changed over time, by the end of 2023, the most prominent narrative was a “return to normal.” Yet, there is less research about people with IDD's quality of life and services and supports beyond the initial waves of the pandemic. The aim of this study was to examine the impact of the COVID‐19 pandemic on people with IDD's quality of life outcomes and supports 4 years into the pandemic. Using a repeated cross‐sectional design and linear and binary logistic regression models, we analyzed secondary Personal Outcome Measures® data from 4549 people with IDD from 2018 to 2023 in the United States ( n = 4391; 32 states), Canada ( n = 142), Ireland ( n = 12), New Zealand ( n = 3), and Australia ( n = 1). We found people with IDD's quality of life outcomes and supports have yet to return to prepandemic levels. In fact, not only was almost every area of quality of life negatively impacted, many people with IDD who were interviewed in 2022 and 2023 had worse outcomes and supports than those interviewed earlier in the pandemic. Instead of clinging to the idea that life or the service system for people with IDD has “returned to normal,” we must recognize that normal was never a good enough destination to return to for people with IDD in the first place. Instead, we must commit to a new normal that is inclusive, accessible, and equitable.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".