Views on the impact of the <scp>COVID</scp>‐19 pandemic on health in people with Down syndrome from diverse backgrounds
Bibliographic record
Abstract
Down syndrome (DS) has a unique medical and psychological profile. To date, few studies have asked individuals with DS about their views of health, and fewer have explored the impact of COVID-19 on the health of individuals with DS and their families. We used a mixed methods approach including two studies on the health of individuals with DS and their parents conducted during the COVID-19 pandemic: (1) eight virtual focus groups, comprised of 20 parents and 8 individuals with DS to obtain participants' views of health, and (2) a 20-item questionnaire on health care experience of patients with DS who are African American or come from primarily Spanish-speaking homes. Focus group transcripts were coded using a hybrid inductive/deductive framework and thematically analyzed using the Framework Method. This questionnaire included questions regarding the impact of COVID-19 on caregivers and their loved ones with DS; responses to these questions were summarized using descriptive statistics. Individuals with DS discussed the impact of the COVID-19 pandemic on their physical and social health including masking, online learning, and online communication with friends and family. Parents of individuals with DS discussed how the COVID-19 pandemic negatively impacted their child's physical, social, and mental health, as a result of virtual schooling and decreased socialization. There were unexpected positives of the pandemic such as improved hygiene and eased scheduling with telehealth visits. Caregivers noted COVID-19 impacted their own anxiety, employment, and other domains that have potential ripple effects on the health of their children. The COVID-19 pandemic had a pervasive impact on the mental health and wellness of caregivers as well as the physical, social, and mental health of individuals with DS.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".