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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.001 | 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".