Chiropractors' characteristics associated with their number of workers' compensation patients.
Bibliographic record
Abstract
STUDY DESIGN: A cross-sectional survey. OBJECTIVE: The purpose of this study was to identify characteristics of Canadian doctors of chiropractic (DCs) associated with their number of workers' compensation patients. SUMMARY OF BACKGROUND DATA: It has been previously hypothesized that DCs that treat a relatively high volume of workers' compensation cases may have different characteristics than the general chiropractic community. METHODS: Secondary data analyses were performed on data collected in the 2011 survey of the Canadian Chiropractic Resources Databank (CCRD). The CCRD survey included 81 questions concerning the practice and concerns of DCs. Of the 6,533 mailed questionnaires, 2,529 (38.7%) were returned. Of these, 652 respondents did not meet our inclusion criteria, and our final study sample included 1,877 respondents. Bivariate analyses were conducted between predetermined independent variables and the annual number of workers' compensation patients. A negative binomial multivariate regression was performed to identify significant factors associated with the number of workers' compensation patients. RESULTS: On average, DCs received 10.3 (standard deviation (SD) = 17.6) workers' compensation cases and nearly one-third did not receive any such cases. The type of clinic (other than sole provider), practice area population (smaller than 500,000), practice province (other than Quebec), number of practice hours per week, number of treatments per week, main sector of activity (occupational/ industrial), care provided to patients (electrotherapy, soft-tissue therapy), percentage of patients with neuromusculoskeletal conditions, and percentage of patients referred by their employer or a physician were associated with a higher annual number of workers' compensation cases. CONCLUSION: Canadian DCs who reported a higher volume of workers' compensation patients had practices oriented towards the treatment of injured workers, collaborated with other health care providers, and facilitated workers' access to care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".