Factors associated with recovery expectations following vehicle collision: A population-based study
Bibliographic record
Abstract
OBJECTIVE: Positive expectations predict better outcomes for a variety of health conditions including recovery from whiplash-associated disorders, but we know little about which individuals have negative expectations, and therefore may be at risk for poor whiplash-associated disorders recovery. METHODS: We assessed expectations for global recovery in a population-based cohort of 6015 individuals with traffic-related whiplash-associated disorders. We used multinomial logistic regression analysis to model factors associated with expecting to recover slowly, or not recover at all, as opposed to expecting to recover quickly. RESULTS: Depressive symptomatology, lower education, lower income, male gender, younger age, being a passenger in the vehicle, history of neck pain, and greater initial pain (greater percentage of body in pain, greater intensity of neck pain and presence of low back and/or headache pain) were associated with poor expectations for recovery. CONCLUSION: A number of demographic, socioeconomic and injury-related factors were associated with expectations for recovery in whiplash-associated disorders. Two of the strongest associated factors were depressive symptomatology and initial neck pain intensity. These results support using a biopsychosocial approach to evaluate expectancies and their influence on important health outcomes.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".