The Influence of “Labels” for Neck Pain on Recovery Expectations Following a Motor Vehicle Crash: An Online-Randomized Vignette-Based Experiment
Bibliographic record
Abstract
OBJECTIVES: To (1) investigate whether different labels for neck pain after a motor vehicle crash (MVC) influenced recovery expectations and management beliefs, (2) explore reasons for low recovery expectations and greater likelihood for lodging a claim, and (3) explore the moderating effect of neck pain history and sociodemographic characteristics. DESIGN: Online randomized experiment with nested qualitative content analysis. METHODS: We randomized 2229 participants from the general population (mean age: 46.7 ± 17.5 years; 72.4% females; 66% with previous or current neck pain; 10% with an MVC experience) to read 1 of 5 scenarios describing a patient with neck pain after an MVC, each was labeled as whiplash injury, whiplash-associated disorder, posttraumatic neck pain, neck pain, or neck strain. The primary outcome was recovery expectations, rated on a 0- to 10-point scale. RESULTS: Participants allocated to whiplash-associated disorder or neck pain had lower recovery expectations than those allocated to neck strain (adjusted mean difference [95% confidence interval]: −0.5 [−0.9 to −0.1] for both comparisons). Whiplash-associated disorder led to more recovery uncertainty, while neck pain led to greater doubt about the health care provider. Most secondary outcomes showed significant but small differences. Participants allocated to neck strain were less inclined to claim than those allocated to whiplash-associated disorder or whiplash injury due to less perceived need for financial support. Neck pain history moderated labeling effects on recovery expectations; household income moderated the claim intention. CONCLUSIONS: Labels for neck pain after an MVC influenced recovery expectations and management preferences. The clinical relevance of the small effects was unclear. J Orthop Sports Phys Ther 2024;54(11):711-720. Epub 5 September 2024. doi:10.2519/jospt.2024.12590
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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 | 0.011 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".