Psychosocial and socio-demographic factors associated with outcomes for patients undergoing rehabilitation for chronic whiplash associated disorders: A pilot study
Bibliographic record
Abstract
PURPOSE: Identify psychosocial and socio-demographic factors (measured prior to treatment) that were associated with post-treatment self-perceived pain and disability and two secondary outcomes: psychological distress, and return to work in patients undergoing multidisciplinary rehabilitation for chronic whiplash associated disorders (WAD). METHOD: Interviews were conducted with 28 patients with chronic WAD at entry to and completion of an intensive rehabilitation program, and a telephone interview was carried out three months later. Participants completed pain and disability, and psychological distress questionnaires, at baseline and at both follow-ups. They also completed psychosocial questionnaires and provided socio-demographic information. The effect of each of the independent variables on the outcomes was first evaluated by simple regressions, and then subsequently by multiple regression analysis. RESULTS: Higher baseline pain and disability predicted higher pain and disability at both follow-ups (p < 0.001), and higher psychological distress at program completion (p = 0.003). Younger age (p = 0.028) and higher baseline psychological distress (p = 0.002) were associated with higher psychological distress three months post-rehabilitation. Greater social support at work was prognostic of return to work at program completion (p = 0.04). CONCLUSIONS: Baseline pain and disability was the only factor that affected pain and disability post-rehabilitation. Psychosocial factors played a role in the prognosis of psychological distress and return to work.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".