Biopsychosocial Multivariate Predictive Model of Occupational Low Back Disability
Bibliographic record
Abstract
STUDY DESIGN: To establish outcome, 253 workers with subacute and chronic low back conditions were assessed with a comprehensive multimethod biopsychosocial protocol at baseline, 3 days after the initial examination, and 3 months later. OBJECTIVE: To validate empirically a biopsychosocial model for prediction of occupational low back disability. SUMMARY OF BACKGROUND DATA: Costs of low back occupational disability continue to spiral despite stabilization of low back injury rates. An empirically based model to predict occupational disability in workers with low back injuries is required. METHODS: Workers with subacute low back injuries (4-6 weeks after injury, n = 192) and those with chronic back pain (6-12 months after injury, n = 61) were the study participants. The biopsychosocial protocol included five groups of variables: 1) sociodemographic, 2) medical, 3) psychosocial, 4) pain behavior, and 5) workplace-related factors. Predictive validity was investigated through a 3-month follow-up assessment, at which time the return to work outcome was determined. Stepwise logistic regression models were developed to predict work status. RESULTS: The final integrated model consisted of variables from a wide biopsychosocial spectrum: vitality, health transition, feeling that job is threatened due to injury, expectations of recovery, guarding behavior, perception of severity of disability, time to complete walk, and right leg typical sciatica. CONCLUSIONS: The "winning" variables identified in the integrated model are dominated by cognitions, which are accompanied by disability behaviors. A cognitive-behavioral model with an adaptation-oriented rather than a pathology-oriented focus is favored for early intervention with high-risk workers since cognitions are amenable to change.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".