Tornado Hazard Communication Disparities among Spanish-Speaking Individuals in an English-Speaking Community
Bibliographic record
Abstract
BACKGROUND: The state of Oklahoma, known for destructive tornados, has a native Spanish-speaking (NSS) population of approximately 180,241, of which 50% report being able to speak English "very well" (US Census Bureau). With almost 50% of these native Spanish-speaking persons being limited English proficient (LEP), their reception of tornado hazard communications may be restricted. This study conducted in northeast Oklahoma (USA) evaluates the association between native language and receiving tornado hazard communications. METHODS: This study was a cross-sectional survey conducted among a convenience sample of NSS and native English-speaking (NES) adults at Xavier Clinic and St. Francis Trauma Emergency Center in Tulsa, OK, USA from September 2009 through December 2009. Of the 82 surveys administered, 80 were returned, with 40 NES and 40 NSS participants. A scoring system (Severe Weather Information Reception (SWIR)) was developed to quantify reception of hazard information among the study participants (1-3 points=poor reception, 4-5=adequate reception, 6-8=excellent reception). Pearson's chi-squared test was used to calculate differences between groups with Yates' continuity correction applied where appropriate, and SWIR scores were analyzed using ANOVA. P-values<.05 were considered significant. RESULTS: NSS fluency in English was 25.6%. No significant association was found between native language and those who watch television, listen to radio, have a National Oceanic and Atmospheric Administration (NOAA) All Hazards radio or telephone, or are in audible range of a tornado siren. NSS were less likely to have Internet access (P<.004), and less likely to know of local telephone warning programs (P<.03). The mean NSS SWIR score was 3.2 (95% CI, 2.8-3.7) while LEP NSS averaged 2.8 (95% CI, 2.4-3.2). The mean NES SWIR score was 4.5 (95% CI, 4.1-5.0). CONCLUSION: Results demonstrate a disparity in tornado warning reception between NSS and NES. Poor English proficiency was noted to be 75% among NSS, which is approximately 25% more than estimated by the US Census Bureau. This study demonstrates a need for emergency managers to recognize when appropriate and overcome communication disparities among limited English proficient populations.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".