The Impact of Residential Greenness on Psychological Distress among Hurricane Katrina Survivors
Bibliographic record
Abstract
Keywords: green space, mental health, disasters, extreme weather Background: Hurricanes are potentially traumatic events that can produce long-lasting health issues for survivors. Research shows that neighborhood-level social features, such as social and economic capital, are associated with lower risk of mental disorders among survivors. However, to our knowledge, there is no work assessing the impact of neighborhood-level environmental features, such as greenness. We hypothesize that, like community social features, the salutary effects of environmental features may modify recovery among disaster-affected populations. Methods: We used data from the Resilience in Survivors of Katrina Study, a cohort assembled in 2004-2005 (time 0 [T0]) of low-income parents in New Orleans, Louisiana, USA, most of whom later experienced Hurricane Katrina. We obtained data on psychological distress (Kessler [K]-6 scores), sociodemographics, and hurricane exposure for 214 participants who were interviewed again in 2006-2007 (T1) and in 2016-2018 (T2). We assessed greenness using average growing season Normalized Difference Vegetation Index (NDVI) in 300-m buffers around participants’ homes at each timepoint and estimated neighborhood concentrated disadvantage (a common composite metric for neighborhood-level socioeconomic status) at the Census tract-level. We assessed the impact of residential greenness on psychological distress among Katrina survivors using adjusted linear regressions with sandwich clustering. Results: Preliminary results found that residential greenness was not significantly associated with distress at any time point. However, moving to a greener neighborhood immediately after Katrina (T1) was associated with a reduction in K6 scores, indicating lessened psychological distress (-1.43 [95% CI: -2.82, -0.04]) compared to moving to a neighborhood with the same or lower greenness. We found similar but non-significant associations at T2. Discussion: In this disaster-affected population, moving to a greener neighborhood was associated with reduced psychological distress in the short term. These findings suggest that neighborhood environmental features may provide temporary health benefits for disaster survivors.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".