French Validation of Two Questionnaires Measuring Posttraumatic Cognitions After Sexual Assault
Bibliographic record
Abstract
People who have been sexually assaulted are at risk of developing symptoms of posttraumatic stress disorder (PTSD), including negative posttraumatic cognitions such as self-blame. Although targeting maladaptive cognitions is an important part of cognitive-behavioral treatment for PTSD, no validated questionnaire targeting posttraumatic cognitions after a sexual assault is available in French. The aim of this study was to translate the Posttraumatic Maladaptive Beliefs Scale (PMBS) and the Rape Attribution Questionnaire (RAQ) in French, adapt the content of the latter to make it more inclusive of all genders and widen the representation of sexual assault contexts, and document their psychometric qualities in the general population who self-reported having experienced a sexual assault. Participants were 439 adults recruited via e-mails to the members of the Laval University community and postings on social media. They completed online sociodemographic and clinical questionnaires, including the PMBS, the RAQ and four questionnaires targeting PTSD symptom severity, posttraumatic cognitions, depression, and anxiety symptoms. The PMBS and the RAQ were completed a second time one week later. The French versions of the questionnaires showed good convergent and divergent validity, internal consistency (PMBS: α between .740 and .868; RAQ: .806 and .901) and test-retest reliability (r between .785 and .833 for the PMBS, .776 and .840 for the RAQ). The French versions of the PMBS and the RAQ are useful instruments to assess posttraumatic cognitions and can be used in clinical and research settings to improve the treatment of PTSD in people who have been sexually assaulted.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".