New Orleans Medical Students post-Katrina — an Assessment of Psychopathology and Anticipatory Transference of Resilience
Bibliographic record
Abstract
Wars, conquest, famines, plagues, earthquakes, tsunamis, forest fires, cyclones, and hurricanes have the potential to cause mass migrations. Hurricane Katrina caused one of the most significant non-war-related migrations in modern history. Not since the Dust Bowl of the 1930s, where residents of the Central Plains states of the United States and Canada were forced to permanently migrate, have more than 1 million people been forced to temporarily or permanently relocate. Even more unique is the fact that the evacuation from eastern and western Louisiana, because of Hurricanes Katrina and Rita, respectively, occurred so quickly, compared with the slower sustained relocation in the 1930s. ABOUT THE AUTHORS Harold M. Ginzburg, MD, JD, MPH, is with the Department of Psychiatry and Neurology, Tulane University Health Sciences Center; Department of Psychiatry, Louisiana State University Health Sciences Center; and Department of Psychiatry, Uniformed Services University of the Health Sciences School of Medicine. David J. Bateman is a 3rd-year medical student at Tulane University Medical School. Address correspondence to Harold M. Ginzburg, MD, Clinical Professor of Psychiatry Suite 200, 3340 Severn Avenue, Metairie, LA 70002; fax 504-613-4913; e-mail haroldginzburg@hotmail.com. Dr. Ginzburg and Mr. Bateman have disclosed no relevant financial relationships. An acknowledgment to Jan Johnson, MD, for obtaining permission for me to conduct the study, and to Jeff Rouse, MD, for assisting in the development of the mental health questionnaire.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".