The longitudinal relationship between post-traumatic stress disorder and perceived social support in survivors of traumatic injury
Bibliographic record
Abstract
BACKGROUND: Although perceived social support is thought to be a strong predictor of psychological outcomes following trauma exposure, the temporal relationship between perceived positive and negative social support and post-traumatic stress disorder (PTSD) symptoms has not been empirically established. This study investigated the temporal sequencing of perceived positive social support, perceived negative social support, and PTSD symptoms in the 6 years following trauma exposure among survivors of traumatic injury. METHOD: Participants were 1132 trauma survivors initially assessed upon admission to one of four Level 1 trauma hospitals in Australia after experiencing a traumatic injury. Participants were followed up at 3 months, 12 months, 24 months, and 6 years after the traumatic event. RESULTS: Latent difference score analyses revealed that greater severity of PTSD symptoms predicted subsequent increases in perceived negative social support at each time-point. Greater severity of PTSD symptoms predicted subsequent decreases in perceived positive social support between 3 and 12 months. High levels of perceived positive or negative social support did not predict subsequent changes in PTSD symptoms at any time-point. CONCLUSIONS: Results highlight the impact of PTSD symptoms on subsequent perceived social support, regardless of the type of support provided. The finding that perceived social support does not influence subsequent PTSD symptoms is novel, and indicates that the relationship between PTSD and perceived social support may be unidirectional.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".