Invisible women: unpacking the erasure of Native American women with intellectual and/or developmental disabilities in health surveillance
Bibliographic record
Abstract
Public health authorities recognize the need for more robust data systems to characterize health inequities, particularly among those with intersectional identities (National Center for Health Statistics, Citation2023). Currently, it is difficult to describe the health outcomes of Native American women with intellectual and/or developmental disabilities (I/DD), a population for whom key demographic information is unavailable. In this qualitative study, we interviewed 11 experts to understand why this population is not represented in mainstream health surveillance. The findings indicate that the visibility of Native American women with I/DD is influenced by both the institutions that shape data collection and the forms of data that are prioritized in health surveillance. Interview participants highlighted the disability service system and Tribal Nations as important gatekeepers of health data, while pointing out structural constraints that prevent these institutions from meeting the data needs of their constituents. Moreover, participants suggested that Western data systems, which prioritize deficit-based models of disability and rely on quantitative methodology, misrepresent people with disabilities and fail to acknowledge Indigenous ways of knowing. Interviews revealed several pathways to improving data equity, beginning with greater representation of Native American people and people with I/DD in institutions that govern health surveillance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".