Overt social support behaviors: Associations with PTSD, concurrent depressive symptoms and gender.
Bibliographic record
Abstract
Women are twice as likely as men to develop a posttraumatic stress disorder (PTSD). Gender differences in social support after a traumatic event might partially explain this disparity. However, the portrait of the links among PTSD, depression, social support, and gender is still unclear. This study examined behaviors of individuals with PTSD and their significant other in relation to PTSD and concurrent depressive symptoms, and tested gender as a moderator of these associations. Observed overt supportive and countersupportive behaviors of 68 dyads composed of an individual with PTSD and a significant other in a trauma-oriented discussion were coded with a support coding system and analyzed according to gender. Gender was revealed to act as a moderator of the links between interactional behaviors of individuals with PTSD and their concurrent depressive symptoms. More specifically, women were less implicated and less likely to propose positive solutions compared with men. On the other hand, men were more implicated and less likely to criticize their significant other than were women. PTSD and concurrent depressive symptoms were related to poorer interpersonal communication in women. Hence, women and men with PTSD and concurrent depressive symptoms might benefit from gender-tailored interventions targeting symptoms and dyadic behaviors.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".