Understanding physicians' attitudes toward people with Down syndrome
Bibliographic record
Abstract
Understanding attitudes of physicians toward people with Down syndrome is important because of the influence physicians have on the future of individuals with Down syndrome. However, few previous studies have assessed these attitudes. Using data from the 2008 DocStyles(©) survey, an annual online survey conducted in the United States, we assessed attitudes of physicians toward people with Down syndrome using a survey that included questions about opinions toward inclusive educational settings and workplaces, previous relationships with people with Down syndrome, and comfort in providing them with medical care. Approximately 20% of participants agreed that students with Down syndrome should go to special schools, and nearly a quarter agreed that including students with Down syndrome in regular classrooms is distracting. While 76.0% of respondents felt comfortable providing medical care to people with Down syndrome, 9.8% reported feeling uncomfortable, and 14.3% reported feeling neutral. Results showed that attitudes that supported inclusion and comfort with providing medical care were more commonly reported among non-Hispanic white physicians, those who had previous relationships with people with Down syndrome, pediatricians, and physicians working in a group or hospital setting. These data are helpful to guide the development of training materials and curricula for healthcare providers regarding Down syndrome.
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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 | 0.012 |
| 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) | 0.003 | 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".