Access to healthcare for deaf people: a model from a middle-income country in Latin America
Bibliographic record
Abstract
OBJECTIVE: To determine if there are existing healthcare access inequities among the deaf Chilean population when compared to the general Chilean population. METHODS: Data were obtained from a population-based national survey in Chile. In total, 745 prelingually deaf individuals were identified. The number of times the person used the healthcare system was dichotomized and analyzed using a multivariate logistic regression model. RESULTS: Prelingually deaf people had lower incomes, fewer years of education, and greater rates of unemployment and poverty when compared with the general population. Moreover, they visited more general practitioners, mental health specialists, and other medical specialists. On average, they attended more appointments for depression but had fewer general checkups and gynecological appointments than the general population. CONCLUSIONS: Deaf people in Chile have a lower socioeconomic status than the rest of the Chilean population. The results from this study are similar to the findings reported for high-income countries, despite differences in the magnitude of the associations between being deaf and healthcare access. Further studies should be conducted to determine the health status of deaf people in Chile and other Latin American countries and what factors are associated with a significantly lower prevalence of gynecological appointments among deaf women when compared with non-deaf women.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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; a candidate call from one teacher head, 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".