Therapeutic treatments for PTSD : does type of treatment impact help seeking behaviors in a military sample?
Bibliographic record
Abstract
There are several known barriers that people face that decrease the likelihood of seeking professional psychological help. The present study sought to identify whether certain treatment types for PTSD serve as barriers to seeking psychological help. It specifically sought to identify trauma-focused treatments as potential barriers due to their perception of being emotionally challenging. A survey was administered to 84 respondents. Of the respondents, 41 were randomly assigned to read a treatment protocol for an exposure-based, trauma-focused psychotherapy for PTSD, which 43 were randomly assigned to read a protocol for a trauma-avoidant psychotherapy for PTSD. Measures of attitudes toward seeking help and mental health stigma were then administered, with treatment type serving as two levels of an independent variable. We hypothesized that participants in the trauma-focused condition would subsequently report higher levels of stigma and more negative attitudes toward seeking help. MANCOVA results did not support our hypothesis as both groups were shown to have equal reactions to the protocols. This held true when controlling for four potential covariates: PTSD symptoms, avoidant coping styles, conformity to masculine gender norms, and previous PTSD treatment history. Treatment implications and future directions were discussed.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".