Alcohol Use in Polish 9/11 Responders
Bibliographic record
Abstract
More than 35,000 individuals are estimated to have responded to the World Trade Center (WTC) site following the terrorist attacks of September 11, 2001. The federally funded WTC Medical Monitoring and Treatment Program (WTCMMTP) provides medical monitoring and occupational medicine treatment as well as counseling regarding entitlements and benefits to the workers and volunteers who participated in the WTC response. A major component of the WTCMMTP is the WTC Mental Health Program (WTCMHP), which offers annual mental health assessments and ongoing treatment for those found to have 9/11 associated mental health problems. In the program's 9.5 years of evaluating and treating mental health problems in thousands of Ground Zero responders, diversity in multiple domains (e.g., gender, family, profession and employment status, state of physical health, cultural identity, and immigration status) has been a hallmark of the population served by the program. To illustrate the types of issues that arise in treating this diverse patient population, the authors first present a representative case involving a Polish asbestos worker with an alcohol use disorder. They then discuss how accepted alcohol treatment modalities can and often must be modified in providing psychiatric treatment to Polish responders, in particular, and to foreign-born patients in general. Treatment modalities discussed include cognitive and behavioral therapy, relapse prevention strategies, psychodynamic therapy, motivational approaches, family therapy, group peer support, and pharmacotherapy. Implications for the practice of addiction psychiatry, cultural psychiatry, and disaster psychiatry are discussed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".