The Importance of Effect Measure Modification When Using Demographic Variables to Predict Evacuation
Bibliographic record
Abstract
Abstract INTRODUCTION: Previous studies have identified a number of demographic characteristics (e.g., age, income, level of education, household composition, and race or ethnicity) that affect hurricane evacuation. However, the magnitude and direction of these associations vary widely, even when the area of landfall or the intensity of the storm is similar. We propose that the associations of demographic characteristics and hurricane evacuation are modified by psychosocial factors such as social cohesion, social capital, and social control. Additional variability may be the result of the changing prevalence of these demographic variables over time or between study locations. METHODS: Ninety census blocks in three eastern North Carolina counties affected by Hurricane Isabel were selected probability proportionate to population and seven interviews were conducted at random locations within each of the selected blocks. Risk differences (RD) and 95% confidence intervals (CI) were produced for stratified data to test for heterogeneity. RESULTS: There was statistical evidence of effect measure modification on the additive scale of the effect of home type, homeownership, age, race, gender, marital status, and having children under age 18 living at home on hurricane evacuation based on Wald p‐values of the interaction terms of ≤ 0.20 and strata‐specific RDs which crossed the null value. Social cohesion, volunteerism, property preparation, church attendance, neighbor's evacuation, and the number of local friends and family modified the RDs for the demographic characteristics. CONCLUSIONS: The associations between demographic characteristics and hurricane evacuation failure are modified by social factors. Effect measure modification on the additive scale may help explain the inconsistency of previously published results and is the appropriate measure for targeted interventions that can increase evacuation among certain groups that are missed when average risks are calculated across the population.
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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 | 0.462 | 0.560 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.013 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".