Long-term Recovery From Hurricane Sandy: Evidence From a Survey in New York City
Bibliographic record
Abstract
OBJECTIVES: This study aimed to examine a range of factors influencing the long-term recovery of New York City residents affected by Hurricane Sandy. METHODS: In a series of logistic regressions, we analyzed data from a survey of New York City residents to assess self-reported recovery status from Hurricane Sandy. RESULTS: General health, displacement from home, and household income had substantial influences on recovery. Individuals with excellent or fair health were more likely to have recovered than were individuals with poor health. Those with high and middle income were more likely to have recovered than were those with low income. Also, individuals who had not experienced a decrease in household income following Hurricane Sandy had higher odds of recovery than the odds for those with decreased income. Additionally, displacement from the home decreased the odds of recovery. Individuals who applied for assistance from the Build it Back program and the Federal Emergency Management Agency had lower odds of recovering than did those who did not apply. CONCLUSIONS: The study outlines the critical importance of health and socioeconomic factors in long-term disaster recovery and highlights the need for increased consideration of those factors in post-disaster interventions and recovery monitoring. More research is needed to assess the effectiveness of state and federal assistance programs, particularly among disadvantaged populations. (Disaster Med Public Health Preparedness. 2018;12:172-175).
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".