PTSD in the Year Following Sexual Assault: A Meta-Analysis of Prospective Studies
Bibliographic record
Abstract
OBJECTIVE: Sexual assault is associated with higher rates of posttraumatic stress disorder (PTSD) than other traumas, and the course of PTSD may differ by trauma type. However, the course of PTSD after sexual assault has not been summarized. The aim of this meta-analysis was to identify the prevalence and severity of PTSD and changes to the average rate of recovery in the 12 months following sexual assault. METHOD: Authors searched four databases for prospective studies published before April 2020 and sought relevant unpublished data. Eligible studies assessed PTSD in at least 10 survivors of sexual assault in at least two time points, starting within 3 months postassault. Random effects linear-linear piecewise models were used to identify changes in average recovery rate and produce model-implied estimates of monthly point prevalence and mean symptom severity. RESULTS: = 2,106) indicated that 74.58% (95% confidence interval [CI]: [67.21, 81.29]) and 41.49% (95% CI: [32.36, 50.92]) of individuals met diagnostic criteria for PTSD at the first and 12th month following sexual assault, respectively. PTSD symptom severity was 47.94% (95% CI: [41.27, 54.61]) and 29.91% (95% CI: [23.10, 36.73]) of scales' maximum severity at the first and 12th month following sexual assault, respectively. Most symptom recovery occurred within the first 3 months following sexual assault, after which point the average rate of recovery slowed. CONCLUSIONS: Findings indicate that PTSD is common and severe following sexual assault, and the first 3 months postassault may be a critical period for natural recovery.
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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 | 0.015 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.054 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".