Exploring Social Determinants of Posttraumatic Pain, Distress, Depression, and Recovery Through Cross-Sectional, Longitudinal, and Nonlinear Trends
Bibliographic record
Abstract
OBJECTIVES: Pain, distress, and depression are predictors of posttrauma pain and recovery. We hypothesized that pretrauma characteristics of the person could predict posttrauma severity and recovery. METHODS: Sex, age, body mass index, income, education level, employment status, pre-existing chronic pain or psychopathology, and recent life stressors were collected from adults with acute musculoskeletal trauma through self-report. In study 1 (cross-sectional, n=128), pain severity was captured using the Brief Pain Inventory (BPI), distress through the Traumatic Injuries Distress Scale (TIDS) and depression through the Patient Health Questionnaire-9 (PHQ-9). In study 2 (longitudinal, n=112) recovery was predicted using scores on the Satisfaction and Recovery Index (SRI) and differences within and between classes were compared with identify pre-existing predictors of posttrauma recovery. RESULTS: Through bivariate, linear and nonlinear, and regression analyses, 8.4% (BPI) to 42.9% (PHQ-9) of variance in acute-stage predictors of chronicity was explainable through variables knowable before injury. In study 2 (longitudinal), latent growth curve analysis identified 3 meaningful SRI trajectories over 12 months. Trajectory 1 (start satisfied, stay satisfied [51%]) was identifiable by lower TIDS, BPI, and PHQ-9 scores, higher household income and less likely psychiatric comorbidity. The other 2 trajectories (start dissatisfied, stay dissatisfied [29%] versus start dissatisfied, become satisfied [20%]) were similar across most variables at baseline save for the "become satisfied" group being mean 10 years older and entering the study with a worse (lower) SRI score. DISCUSSION: The results indicate that 3 commonly reported predictors of chronic musculoskeletal pain (BPI, TIDS, PHQ-9) could be predicted by variables not related to the injurious event itself. The 3-trajectory recovery model mirrors other prior research in the field, though 2 trajectories look very similar at baseline despite very different 12-month outcomes. Researchers are encouraged to design studies that integrate, rather than exclude, the pre-existing variables described here.
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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 | 0.003 | 0.006 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".