The impact of residential greenness on psychological distress among Hurricane Katrina survivors
Bibliographic record
Abstract
Residential greenness may support mental health among disaster-affected populations; however, changes in residential greenness may disrupt survivors' sense of place. We obtained one pre- and three post-disaster psychological distress scores (Kessler [K]-6) from a cohort (n = 229) of low-income mothers who survived Hurricane Katrina in New Orleans, Louisiana, USA. Greenness was assessed using average growing season Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) in the 300 m around participants' homes at each time point. We used multivariable logistic regressions to evaluate two hypotheses: 1) that cross-sectional greenness (above vs. below median) was associated with reduced psychological distress (K6≥5); and 2) that changes in residential greenness were associated with adverse mental health. When using EVI, we found that a change in level of greenness (i.e., from high to low [high-low], or from low to high [low-high] greenness, comparing pre- and post-Katrina neighborhoods) was associated with increased odds of distress at the first post-storm survey, compared to moving between or staying within low greenness neighborhoods (low-high odds ratio [OR] = 3.48; 95% confidence interval [CI] = 1.40, 8.62 and high-low OR = 2.60; 95% CI: 1.05, 6.42). Results for NDVI were not statistically significant. More research is needed to characterize how residential greenness may impact the health of disaster survivors, and how these associations may change over time.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".