Understanding attitudes toward people with Down syndrome
Bibliographic record
Abstract
Understanding attitudes of the public toward people with Down syndrome is important because negative attitudes might create barriers to social integration, which can affect their success and quality of life. We used data from two 2008 U.S. surveys (HealthStyles(c) survey of adults 18 years or older and YouthStyles(c) survey of youth ages 9-18) that asked about attitudes toward people with Down syndrome, including attitudes toward educational and occupational inclusion and toward willingness to interact with people with Down syndrome. Results showed that many adults continue to hold negative attitudes toward people with Down syndrome: A quarter of respondents agreed that students with Down syndrome should go to special schools, nearly 30% agreed that including students with Down syndrome in typical educational settings is distracting, and 18% agreed that persons with Down syndrome in the workplace increase the chance for accidents. Negative attitudes were also held by many youth: 30% agreed that students with Down syndrome should go to separate schools, 27% were not willing to work with a student with Down syndrome on a class project, and nearly 40% indicated they would not be willing to spend time with a student with Down syndrome outside of school. Among both adult and youth, female sex and respondents with previous relationships with people with Down syndrome were consistently associated with more positive attitudes. These results may be helpful in the development of educational materials about Down syndrome and in guiding policies on educational and occupational inclusion.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".