A Population-Based Analysis of Healthcare Utilization of Persons With Back Disorders
Bibliographic record
Abstract
STUDY DESIGN: A retrospective, population-based analysis. OBJECTIVES: To analyze the utilization of a variety of healthcare services for persons with and without a chronic back disorder, and to identify factors associated with specific patterns of healthcare resource use. SUMMARY OF BACKGROUND DATA: Although there have been studies of how chronic back disorders influence the use of specific healthcare services, we do not currently have a broad, population-based overview of how this condition influences healthcare service utilization. METHODS: Person-level data were taken from the 2000-2001 Canadian Community Health Survey (CCHS), a nationwide cross-sectional survey of health determinants, health status, and health system utilization of Canadians. A series of binary logistic regressions examining healthcare resource utilization were performed on a full study sample (n = 113,229), as well as a restricted sample (n = 36,713) with attention focused on subjects with a single diagnosis of a chronic back disorder. RESULTS: Persons with chronic back disorders were more likely to use physician resources (multivariate odds ratio [OR] = 1.2; 95% confidence interval, 1.1-1.2), and nonphysician resources (OR range, 2.1-3.6) compared with persons without the condition, with chiropractic care having an odds ratio of 3.6 (95% confidence interval, 3.5-3.8). Higher socioeconomic status, the presence of activity-limiting pain, and depressive symptoms were associated with a significant increase in utilization of almost all healthcare services. CONCLUSIONS: With increasing disability as indicated by the presence of pain and functional limitations, and the presence of depressive symptoms, the higher the utilization of physician and nonphysician resources, with the exception of chiropractic care, which appears to be used by those with less severe symptoms. Lower socioeconomic status was associated with significantly lower receipt of services for almost all healthcare providers.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".