Personal and Practice Predictors Associated With the Income of Ontario Chiropractors
Bibliographic record
Abstract
OBJECTIVE: Practice-based information may allow policy makers and associations the opportunity to interpret utilization rates and anticipate the impact of future growth of professions. The objective of this study was to assess the association between income and specific personal, practice, and treatment characteristics in a sample of Ontario chiropractors. METHODS: Descriptive and regression analyses were used to assess end-of-year practice summary data obtained from a professional billing software program voluntarily submitted by 731 individual chiropractors. RESULTS: The model explained 65% of the variance in income. Significant explanatory factors regarding income were those related to treatment characteristics, with the largest contribution made by the total number of new patients seen in the year, which uniquely contributed 17% of the total variance. Personal and practice-related characteristics made significant but relatively small contributions; however, the location of the practice and years since graduation appear to impact income, especially in the formative years of practice development. CONCLUSION: The variance in annual practitioner income was predicted by a combination of personal, practice, and treatment characteristics but not surprisingly primarily by the total number of new patients seen in the year. A negative association between average treatment costs and number of patients seen suggests cost sensitivity. The results provide important benchmarks that can be used to guide expectations of new graduates and to assess future trends. Further work is needed to determine if the findings can be generalized.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".