Chiropractors' Characteristics Associated With Physician Referrals: Results From a Survey of Canadian Doctors of Chiropractic
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to identify characteristics of Canadian doctors of chiropractic (DCs) associated with the number of patients referred by medical doctors (MDs). METHODS: Secondary data analyses were performed on the 2011 cross-sectional survey of the Canadian Chiropractic Resources Databank. The Canadian Chiropractic Resources Databank survey included 81 questions about the practice of DCs. Of the 6533 mailed questionnaires, 2529 (38.7%) were returned and 489 did not meet our inclusion criteria. Our analyzed sample included 2040 respondents. Bivariate analyses were conducted between predetermined potential predictors and the annual number of patients referred by MDs, and negative binomial multivariate regression was performed. RESULTS: On average, DCs reported receiving 15.6 (standard deviation, 31.3) patient referrals from MDs per year and nearly one-third did not receive any. The type of clinic (multidisciplinary with MD), the province of practice (Atlantic provinces), the number of treatments provided per week, the number of practicing hours, rehabilitation and sports injuries as the main sector of activity, prescription of exercises, use of heat packs and ultrasound, and the percentage of patients referred to other health care providers were associated with a higher number of MD referrals to DCs. The percentage of patients with somatovisceral conditions, using a particular chiropractic technique (hole in one and Thompson), taking his/her own radiographs, being the client of a chiropractic management service, and considering maintenance/wellness care as a main sector of activity were associated with fewer MD referrals. CONCLUSION: Canadian DCs who interacted with other health care workers and who focus their practice on musculoskeletal conditions reported more referrals from MDs.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".