CONSECUENCIAS PSICOLÓGICAS DE LOS ATENTADOS TERRORISTAS DEL 11-M EN MADRID. PLANTEAMIENTO GENERAL DE LOS ESTUDIOS Y RESULTADOS EN LA POBLACIÓN GENERAL
Bibliographic record
Abstract
On the morning of March 11 of 2004, Madrid city suffered the most violent terrorist attack occurred in the modern European history. Ten bombs exploded in four commuter trains at morning rush hour, when many people went to Madrid down-town, resulted in 191 deaths and 1800 persons in-jured. After terrorist attacks, we started out three longitudinal studies with the aim of assessing the psychological impact of this event in the Madrid city population, and focused on: (1) general population, (2) victims and relatives, and (3) emergency person-nel and rescue workers. Among other variables, it is explored the onset and evolution of different psycho-logical pathologies such as panic attack, PTSD, and depression. In this study a representative sample of Madrid city residents of 1,589 subjects (1,265 resi-dents of the 21 Madrid city districts plus an over-sampling of 324 residents in the affected areas) is addressed. Research design and telephone interview used in this study are similar to those employed by Galea et al. (2002) in the study of September 11, 2001, terrorist attacks in NYC. Among psycho-pathological prevalence rates, there are remarkable data regarding the 10,9% of panic attack, the 8% of major depression, and the 4% of PTSD after March 11, and the 2,3% of PTSD directed attributable to terrorist attacks.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".