Perceived resistance to experiences of trauma and crisis. A study comparing multiple life events.
Bibliographic record
Abstract
Purpose. Subjective perception is considered a key element in the prediction of resistant or vulnerable responses to trauma and crisis. This study aimed to assess the relationship between perceived physical life threat (PT) and perceived life impact (PI) with post-traumatic symptomatology (PTSD), in a sample of 3.565 persons from 12 countries across nine different traumatic events. Methods . Participants were classified into four groups of self-perceived resistance based on their levels of PT and PI. Results . Main results show Non-affected was the most frequent category in natural catastrophes (48.9%), migration (45.9%), motor vehicle accidents (39.83%), and death threats (33.4%). In the case of sexual abuse by a relative or close person (44.5%), sexual abuse by a stranger (33.9%), and having a severe, chronic, or disabling illness (47.3%), the most frequent category was Survivor . For domestic violence, the most frequent category was Vulnerable (45.5%). Resistant was never the most frequent category for any of the events studied. Although gender and lower education predicted PTSD in most events of trauma and crisis, they were a weak predictor of vulnerable versus resistance categories. Conclusion These results suggest that the Perceived Resistance Indicator can provide insights into the narratives of resistance or vulnerability associated with extreme experiences.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".