Social determinants of health in adults with whiplash associated disorders
Bibliographic record
Abstract
Abstract Objectives Although it is well-known that chronic diseases need to be managed within the complex biopsychosocial framework, little is known about the role of sociodemographic features in adults with whiplash-associated disorders (WAD) and their association with health outcomes. The aim of this study was to investigate the association between various sociodemographic features (age, sex, ethnicity, education, working, marriage, caring for dependents, and use of alcohol and drugs) and health outcomes (pain, disability, and physical/mental health-related quality of life) in WAD, both through their individual relationships and also via cluster analysis. Methods Independent t -tests and Kruskal–Wallis tests (with Mann–Whitney tests where appropriate) were used to compare data for each health outcome. Variables demonstrating a significant relationship with health outcomes were then entered into two-step cluster analysis. Results N = 281 participated in study (184 females, mean (±SD) age 40.9 (±10.7) years). Individually, level of education ( p = 0.044), consumption of non-prescribed controlled or illegal drugs ( p = 0.015), and use of alcohol ( p = 0.008) influenced level of disability. Age ( p = 0.014), marriage status ( p = 0.008), and caring for dependents ( p = 0.036) influenced mental health quality of life. Collectively, two primary clusters emerged, with one cluster defined by marriage, care of dependents, working status, and age >40 years associated with improved mental health outcomes ( F 1,265 = 10.1, p = 0.002). Discussion Consistent with the biopsychosocial framework of health, this study demonstrated that various sociodemographic features are associated with health outcomes in WAD, both individually and collectively. Recognizing factors that are associated with poor health outcomes may facilitate positive outcomes and allow resource utilization to be tailored appropriately.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".