Professional and Demographic Profile of Spanish-Speaking Child Neurologists in the United States
Bibliographic record
Abstract
ObjectiveTo ascertain the prevalence of culturally native Spanish-speaking child neurologists in the United States. Methods: Prevalence statistics regarding demographic and work profile were applied to data obtained from a cross-sectional electronic survey of Child Neurology Society (CNS) members. Results: Demographics of the 135 respondents were comparable to a similar CNS survey except for ethnicity as shown in Table 1. Fifty- three percent were male and 24% were over age 60. Approximately a quarter were represented each from East, South, Midwest, and Western US. 42% self-identified as Spanish, Hispanic, or Latino. 62% spoke English as their primary language and 39% spoke Spanish as their primary language. Two-thirds graduated from a US medical school, 51% practice general neurology, and epilepsy was the most common subspecialty (18%). Two-thirds of respondents practice at a major teaching hospital, and 93% hold university academic appointments. 79% are AAN members. 76% did not have medical student debt at the time of the survey. 29% report signs consistent with burnout. 87% would choose Child Neurology again and 96% would recommend Child Neurology to a medical student. Conclusion: 40% of survey respondents self-identified as Hispanic, Latino or Spanish and spoke Spanish as the primary language and the majority practice in Academic Medicine. Nearly a third of those in the current survey identify burnout symptoms. Consideration of distinctive language and cultural characteristics across the US may lead to provision of a more patient-centered and equitable care.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".