Predicting the Physical and Mental Health Status of Individuals With Chronic Musculoskeletal Pain From a Biopsychosocial Perspective
Bibliographic record
Abstract
OBJECTIVES: Chronic pain is theoretically conceptualized from a biopsychosocial perspective. However, research into chronic pain still tends to focus on isolated, biological, psychological, or social variables. Simultaneous examination of these variables in the prediction of outcomes is important because communalities between predictors exist. Examination of unique contributions might help guide research and interventions in a more effective way. METHODS: The participants were 114 individuals with chronic pain (mean age=58.81, SD=11.85; 58.8% women and 41.2% men) who responded to demographics (age and sex), pain characteristics (duration and sensory qualities), psychological (catastrophizing and perceived injustice), and social (marital adjustment) measures. Multivariate analyses were conducted to investigate their unique contributions to pain-related health variables pain severity, pain interference, disability, anxiety, and depressive symptoms. RESULTS: Bivariate analyses evidenced significant associations between pain sensory qualities, catastrophizing, perceived injustice, and all health variables. In multivariate analyses, pain sensory qualities were associated with pain severity (β=0.10; 95% confidence interval [CI]=0.05, 0.14; t=4.28, P<0.001), while perceived injustice was associated with pain interference (β=0.08; 95% CI=0.03, 0.12; t=3.59, P<0.001), disability (β=0.25; 95% CI=0.08, 0.42; t=2.92, P=0.004), anxiety (β=0.18; 95% CI=0.08, 0.27; t=3.65, P<0.001), and depressive symptoms (β=0.14; 95% CI=0.05, 0.23; t=2.92, P=0.004). Age, sex, pain duration, and marital adjustment were not associated with health variables either in bivariate or in multivariate analyses (all P>0.010). DISCUSSION: As expected, communalities between biopsychosocial variables exist, which resulted in a reduced number of unique contributions in multivariate analyses. Perceived injustice emerged as a unique contributor to variables, which points to this psychological construct as a potentially important therapeutic target in multidisciplinary treatment of pain.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| 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 | 0.000 | 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".