Depressive Symptoms in Black and White Volunteers: Six-month Post Deadly Natural Hazard Hurricane: Does Race Identity Matter?
Bibliographic record
Abstract
Natural hazards have become increasingly common in the United States, wherein across the nation residents are exposed to floods, hurricanes, tornadoes, and a host of other events that occur due to changes in the climate. Amid providing care for communities that have encountered a natural hazard, the volunteers and rescuers are also exposed to the trauma caused by the natural hazard. The primary focus of the study was to elucidate differences in mental health symptoms of the volunteers by race and to determine if years of experience with previous trauma predicts and has a relationship with the development of mental health symptoms. A total of 182 social work students from 3 public universities that were from areas impacted during Hurricanes Katrina and Rita and volunteered in the aftermath, consisted of our sample. The participants completed surveys regarding demographics, mental health symptoms, various stressors, and the presence of social support. Depression scores among Black participants were significantly higher (M = 17.74) compared to White participants and participants of younger age were more likely to experience depression. A final statistical model revealed negative emotion among Black participants indicated a decreased likelihood of developing depression when compared to White participants. The findings indicate the importance of providing adequate training and mental health resources for volunteers and particularly Black volunteers in an effort to prevent the occurrence of depression, which could potentially decrease their overall mental health after a natural hazard.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".