A Scoping Review of Preferences of Men Who Experienced Sexual Assault: Implications for Adaptation of Trauma-Focused Cognitive Behavioral Therapies
Bibliographic record
Abstract
About 1 in 10 men experiences sexual assault, resulting in various difficulties most frequently associated with post-traumatic stress disorder. However, trauma-focused cognitive behavioral therapies (TF-CBT) seem less effective for men who experienced sexual assault compared to women. Efficacy of TF-CBT could be improved by adapting interventions according to the empirical data detailing men's preferences regarding psychological services. This scoping review aimed to document preferences of men who experienced sexual assault regarding psychological services, and to explore barriers and motivators to help-seeking for this population. A systematic approach was used to gather literature describing preferences regarding psychological services, and barriers and motivators to help-seeking. Thirty-five peer-reviewed studies and two non-peer reviewed reports met inclusion criteria. Data from included articles were extracted using a systematic extraction grid. A thematic content analysis was conducted to synthesize and present the results from the 37 studies. The number of empirical studies on preferences regarding psychological services was limited as only five documented preferences, all related to the clinician's characteristics (e.g., clinician's gender) and the type of intervention (e.g., action-oriented). Most studies reviewed barriers to help-seeking. The barriers most frequently identified were adherence to masculine norms and to myths about male sexual assault. Injury or substance abuse following sexual assault often act as motivators to help-seeking for men. To adapt TF-CBT to men who experienced sexual assault, researchers and clinicians should accommodate and further study these men's preferences, consider their motivators regarding help-seeking and alleviate barriers to help-seeking, notably by deconstructing masculine norms.
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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 | 0.033 | 0.134 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.021 | 0.023 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 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".