Social causation or social erosion? Evaluating the association between social support and PTSD among Veterans in a transition program
Bibliographic record
Abstract
Introduction: Social support’s association with posttraumatic stress disorder (PTSD) in Veterans is well established. One explanation for this link is social causation – support inhibits PTSD. Inversely, within the social erosion model, PTSD erodes support. The aim of the present study was to examine if the social causation or social erosion model better explained the association between support and PTSD within a psychosocial intervention context. Methods: Veterans ( N = 218) participating in a multimodal transition program were assessed pre-program, post-program, and at 3-month follow-up on their perceived social support and PTSD symptoms. We used path analysis to conduct a three-wave cross-lagged panel model to compare the social erosion and social causation models. Results: PTSD symptoms were associated with attenuated improvements in social support, while social support was not associated with increased reductions in PTSD symptoms. This association was observed from pre- to post-program and from post-program to follow-up. Discussion: These findings support the social erosion model over the social causation model. Clinical implications of PTSD inhibiting interpersonal gains are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".