Pain and PTSD symptoms in female veterans⋆,⋆⋆
Bibliographic record
Abstract
BACKGROUND: There has been growing empirical examination of the co-occurrence of pain and post-traumatic stress disorder (PTSD) symptoms, and existing evidence suggests that the symptoms associated with each have a close association. To date, however, the association has only been examined within samples of mostly male participants. AIM: In the present study, pain and PTSD symptoms were examined in a sample of 221 female veterans who utilised the VA Healthcare System between 1998 and 1999. METHOD: Women who visited the clinic between 1998 and 1999 were mailed a self-report questionnaire package designed to elicit information regarding general health (including pain experiences), military and trauma history, childhood abuse and neglect, and PTSD symptoms. Analyses were conducted to identify differences in pain experience between those women classified as having PTSD, subsyndromal PTSD, and no PTSD. Analyses were also conducted to determine the degree to which pain-related (e.g., current pain, interference with activity) variables predicted PTSD symptom cluster scores. RESULTS: The three groups differed significantly on a number of pain-related variables. Analyses suggested that pain-related variables were significant predictors of PTSD symptom cluster scores. CONCLUSIONS: These results indicate that the association between pain and PTSD symptoms, previously observed in primarily male samples, is generalisable to females. Clinical implications and possible mechanisms of association are 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.004 | 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".