The mental health and well‐being of adults with intellectual disability during the<scp>COVID</scp>‐19 pandemic: A narrative review
Bibliographic record
Abstract
Prior to the start of the pandemic, adults with intellectual disability (ID) were more likely to experience mental health difficulties and face barriers obtaining mental health care. COVID-related public health restrictions, combined with heightened challenges to obtain timely mental health care, have served to worsen this situation internationally, with a combination of new onset conditions and worsening mental health for those already struggling. In this narrative review, we summarize literature on the mental health of adults with ID during the COVID-19 pandemic to describe what is known based on clinician perceptions, existing administrative health data, family and staff perceptions, and self-report. In addition to noting similarities and differences in findings based on the source of information, we also explored how experiences differed depending on where and when the research was conducted. Based primarily on research conducted during the first six months of the pandemic, there is a consistent finding across sources of increased anxiety, stress, and isolation. This review also explored the delivery of virtual mental health care and the impact of pandemic-based mental health interventions. There have been very few research studies evaluating clinical care during this time but clinicians have managed to provide supports virtually, which has been evaluated positively by some individuals. This narrative review concludes by identifying gaps in the literature and suggests key directions for future mental health research, policy, and practice efforts. Any mental health efforts now and during pandemic recovery need to have an understanding of how the mental health needs and services for adults with ID have evolved over the course of the pandemic. Further research is needed on the impact of both clinical interventions and other nonclinical efforts on the mental health of people with ID.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".