Healthcare experiences of patients with Down syndrome from primarily Spanish‐speaking households
Bibliographic record
Abstract
We report on the health care experiences of individuals with Down syndrome (DS) from families who are primarily Spanish-speaking. Data were collected through three methods: (1) a nationally distributed, 20-item survey, (2) two focus groups with seven family caregivers of individuals with DS who self-identified as living in primarily Spanish speaking households, and (3) 20 interviews with primary care providers (PCPs) who care for patients who are underrepresented minorities. Standard summary statistics were used to analyze the quantitative survey results. Focus group and interview transcripts, as well as an open-ended response question in the survey, were analyzed using qualitative coding methods to identify key themes. Both caregivers and PCPs described how language barriers make giving and receiving quality care difficult. Caregivers additionally described condescending, discriminatory treatment within the medical system and shared feelings of caregiver stress and social isolation. Challenges to care experienced by families of individuals with DS are compounded for Spanish-speaking families, where the ability to build trust with providers and in the health care system may be compromised by cultural and language differences, systemic issues (lack of time or inability to craft more nuanced schedules so that patients with higher needs are offered more time), mistrust, and sometimes, overt racism. Building this trust is critical to improve access to information, care options, and research opportunities, especially for this community that depends on their clinicians and nonprofit groups as trusted messengers. More study is needed to understand how to better reach out to these communities through primary care clinician networks and nonprofit organizations.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| 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".