What are people's perspectives on different labels for neck pain after a motor vehicle crash? A content analysis of randomized study data
Bibliographic record
Abstract
BACKGROUND: Labels for neck pain after a motor vehicle crash (MVC) influenced recovery expectations and management preferences. Research is needed to understand why these expectations and preferences varied based on the label given. AIM: To explore how people perceive different labels for neck pain after an MVC. METHODS: We performed a content analysis of qualitative data from a randomised controlled study. 2229 participants with and without neck pain read a vignette describing a patient with neck pain after an MVC, using one of five labels: whiplash injury, whiplash-associated disorder, post-traumatic neck pain, neck pain, or neck strain. Participants provided free-text responses on the label's meaning, associated words/feelings, required health services/treatments, and any confusion about the label. RESULTS: Compared to neck strain, post-traumatic neck pain, whiplash-associated disorder, and neck pain more commonly evoked negative feelings about symptom severity and prognosis (4.7 % for neck strain versus 7.2 %-16.0 % for other labels) and psychological distress (7.3 % versus 13.0 %-30.3 %). Regarding treatment preference, neck pain most commonly promoted need for passive physical therapies (21.6 %) and imaging (9.8 %), whereas neck strain most often promoted need for exercise (11.6 %) and rarely imaging (3.4 %). Neck pain was the most confusing label (39.9 %), while whiplash injury was the least (14.8 %), with confusion arising from vagueness or a mismatch with diagnostic expectations. CONCLUSION: The meanings, feelings and confusions evoked by neck pain labels after an MVC may explain their impact on recovery expectations and management preferences. Clinicians may consider avoid labels associated with negative feelings and lower preferences for guideline-recommended treatments.
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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 | 0.181 | 0.440 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".