What Influences Positive Return to Work Expectation?
Bibliographic record
Abstract
In Brief Study Design. Cross-sectional study of population-based traffic cohort. Objective. To determine which factors are associated with both positive and negative expectations for returning to work after vehicle collision resulting in neck pain. Summary of Background Data. Positive expectations predict better outcomes for a variety of health conditions, including return to work from soft-tissue injury (including whiplash-associated disorders [WADs]). However, we know little about those with negative expectations who may be at risk for poor WAD outcomes. Methods. We assessed expectations for return to work in a population-based cohort of 2335 individuals with traffic-related WAD. We used logistic regression analysis to model factors associated with expecting to return to work (compared with not expecting to return to work or being unsure). Results. Depressive symptomatology, lower education, lower income, male sex, and greater initial pain (greater percentage of body in pain and greater intensity of neck pain) were associated with lower return-to-work expectation. Conclusion. A number of demographic, socioeconomic, and injury-related factors were associated with expectations for return to work in WAD. Two of the strongest associated factors were depressive symptomatology and postcollision initial neck pain intensity. These results support using a biopsychosocial approach to evaluate expectancies and their influence on important health outcomes. To determine which factors are associated with both positive and negative expectations for returning to work after vehicle collision, we performed a cross-sectional study in a population-based cohort and found that pain and demographic and psychologic variables were statistically associated with return to work expectation.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".