The chiropractic profession: a scoping review of utilization rates, reasons for seeking care, patient profiles, and care provided
Bibliographic record
Abstract
Background: Previous research has investigated utilization rates, who sees chiropractors, for what reasons, and the type of care that chiropractors provide. However, these studies have not been comprehensively synthesized. We aimed to give a global overview by summarizing the current literature on the utilization of chiropractic services, reasons for seeking care, patient profiles, and assessment and treatment provided. Methods: Systematic searches were conducted in MEDLINE, CINAHL, and Index to Chiropractic Literature using keywords and subject headings (MeSH or ChiroSH terms) from database inception to January 2016. Eligible studies: 1) were published in English or French; 2) were case series, descriptive, cross-sectional, or cohort studies; 3) described patients receiving chiropractic services; and 4) reported on the following theme(s): utilization rates of chiropractic services; reasons for attending chiropractic care; profiles of chiropractic patients; or, types of chiropractic services provided. Paired reviewers independently screened all citations and data were extracted from eligible studies. We provided descriptive numerical analysis, e.g. identifying the median rate and interquartile range (e.g., chiropractic utilization rate) stratified by study population or condition. Results: The literature search retrieved 14,149 articles; 328 studies (reported in 337 articles) were relevant and reported on chiropractic utilization (245 studies), reason for attending chiropractic care (85 studies), patient demographics (130 studies), and assessment and treatment provided (34 studies). Globally, the median 12-month utilization of chiropractic services was 9.1% (interquartile range (IQR): 6.7%-13.1%) and remained stable between 1980 and 2015. Most patients consulting chiropractors were female (57.0%, IQR: 53.2%-60.0%) with a median age of 43.4 years (IQR: 39.6-48.0), and were employed (median: 77.3%, IQR: 70.3%-85.0%). The most common reported reasons for people attending chiropractic care were (median) low back pain (49.7%, IQR: 43.0%-60.2%), neck pain (22.5%, IQR: 16.3%-24.5%), and extremity problems (10.0%, IQR: 4.3%-22.0%). The most common treatment provided by chiropractors included (median) spinal manipulation (79.3%, IQR: 55.4%-91.3%), soft-tissue therapy (35.1%, IQR: 16.5%-52.0%), and formal patient education (31.3%, IQR: 22.6%-65.0%). Conclusions: This comprehensive overview on the world-wide state of the chiropractic profession documented trends in the literature over the last four decades. The findings support the diverse nature of chiropractic practice, although common trends emerged.
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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 | 0.020 | 0.086 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.047 | 0.048 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".