Association Between Characteristics of Danish Chiropractors and Number of Referred Patients From General Practitioners: A Cross-sectional Study
Bibliographic record
Abstract
OBJECTIVE: The purposes of this study were to quantify the number of patients referred weekly from primary health care general practitioners (GPs) to chiropractors and to identify chiropractor characteristics associated with the number of referrals. METHODS: An online, cross-sectional survey was sent to the 600 active members of the Danish Chiropractic Association. Of those, 364 completed the survey and 286 met our inclusion criteria. We used bivariable analyses and negative binomial multivariable regression to evaluate the association between predetermined characteristics and the weekly number of patients referred by GPs. RESULTS: On average, chiropractors reported that they received 2.5 (standard deviation, 2.2) referrals from GPs per week, representing 31% of all new patients received by chiropractors. Clinics with more than 1 chiropractor and access to diagnostic ultrasound received more GP referrals. Chiropractors reporting expertise in geriatrics and a higher number of new patients per week reported more referrals. Chiropractors who reported referring more patients to GPs also reported receiving more referrals from them. CONCLUSION: Variables related to access to chiropractic care, the type of care provided, and interprofessional communication were positively associated with the number of GP referrals that chiropractors self-reported. This is the first study to report on factors associated with GP referrals to Danish chiropractors.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".