Determinants of general health perception among individuals with chronic low back pain overtime: structural equation modeling
Bibliographic record
Abstract
BACKGROUND: Low back pain (LBP) is a public health problem. General health perception is the best predictor of healthcare utilization and mortality. Identifying predictors of health perception helps understand how people with LBP live, implement the appropriate treatment, and improve the quality of care. OBJECTIVE: This study aimed to estimate the relationships between pain intensity, psychological distress, self-efficacy, functional ability, and healthcare utilization among individuals with chronic LBP over a period of six-months and to estimate the impact of these relationships on general health perception. METHODS: This is a secondary analysis of data from a longitudinal study that assessed the health outcomes of individuals with chronic LBP. Structural equation modeling (SEM), based on health frameworks, was used to estimate the predictors of health perception among people with LBP at baseline and 6-months. RESULTS: 314 individuals with LBP were included in the analysis. The final SEM model had good fit statistics and explained 48% of health perception variance at 6-months. The model showed that health perception was significantly affected by pain intensity (β = 0.29, β = 0.21), psychological distress (β = 0.51, β = 0.44) and self-efficacy (β = 0.4, β = 0.36) cross-sectionally and overtime, respectively. CONCLUSION: Different health outcomes could affect the health perception among people with low back pain. This requires holistic approaches to treatment, involving self-management and cognitive behavioral therapy, as well as improved self-efficacy to improve their health.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".