Does the Vulnerability Paradox in PTSD Apply to Women and Men? An Exploratory Study
Bibliographic record
Abstract
Recent research suggests that greater country vulnerability is associated with a decreased, rather than increased, risk of mental health problems. Because societal parameters may have gender‐specific implications, our objective was to explore whether the “vulnerability paradox” equally applies to women and men. Lifetime posttraumatic stress disorder (PTSD) prevalence data for women and men were retrieved from 11 population studies (N = 57,031): conducted in Australia, Brazil, Canada, France, Lebanon, Mexico, Netherlands, Portugal, Sweden, Switzerland, and the United States. We tested statistical models with vulnerability, gender, and their interaction as predictors. The average lifetime PTSD prevalence in women was at least twice as high as it was in men and the vulnerability paradox existed in the prevalence data for women and men (R2 = .70). We could not confirm the possibility that gender effects are modified by socioeconomic and cultural country characteristics. Issues of methodology, language, and cultural validity complicate international comparisons. Nevertheless, this international sample points at a parallel paradox: The vulnerability paradox was confirmed for both women and men. The absence of a significant interaction between gender and country vulnerability implies that possible explanations for the paradox at the country‐level do not necessarily require gender‐driven distinction.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".