Barriers to Change in Depressive Symptoms After Multidisciplinary Rehabilitation for Whiplash
Bibliographic record
Abstract
OBJECTIVE: Depressive symptoms complicate patients' recovery after musculoskeletal injury. There is strong evidence to support the utility of multidisciplinary approaches for treating comorbid pain and depressive symptoms. Despite this, a significant proportion of patients may not experience meaningful reductions in depressive symptoms following intervention. The purpose of this study was to identify barriers to change in depressive symptom during multidisciplinary rehabilitation for patients with whiplash injuries. METHODS: A total of 53 patients with clinically meaningful levels of depressive symptoms before participating in a standardized multidisciplinary rehabilitation program participated in this study. Patients completed self-report measures of depressive symptoms, demographic factors, pain intensity, disability, posttraumatic stress symptoms, pain catastrophizing, perceived injustice, and self-efficacy upon commencement and completion of the rehabilitation program. Analyses examined whether pretreatment variables predicted change in depressive symptoms over treatment and the maintenance of clinically meaningful levels of depressive symptoms at posttreatment. RESULTS: Duration of work absence and perceived injustice were significant unique predictors of percent change in depressive symptoms in a linear regression analysis. Perceived injustice was the only significant unique predictor of the presence of clinically meaningful levels of depressive symptoms at posttreatment in a logistic regression analysis. CONCLUSIONS: The results suggest that the identification of patients with high levels of perceived injustice and implementation of targeted interventions for these patients might contribute to greater improvements in their depressive symptomatology.
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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 | 0.006 |
| 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 | 0.001 |
| 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".