Gender and ethnic diversity and wage gaps in the Canadian chiropractic workforce
Bibliographic record
Abstract
As health systems worldwide continue to face health workforce challenges exacerbated by the Covid-19 pandemic, chiropractors can play an important role in meeting increasing needs for rehabilitation services. However, limited evidence from some countries suggests the chiropractic workforce does not reflect the diversity of the population it serves. This observational study quantifies the chiropractor workforce in Canada in gender and ethnocultural composition and earnings, as tracers of equity and inclusion within this healthcare profession. We used 2021 population census data with integrated administrative income tax records to identify and characterize chiropractic practitioners aged 25–54. Following a descriptive analysis, multivariate regression and Blinder-Oaxaca decomposition methods were applied to assess gender and ethnic earnings differences, adjusting for a range of professional and personal factors. The chiropractic workforce was underrepresented regarding women (44.5% versus 50.6% of the total population) and visible minorities (20.0% versus 26.5%). Despite similar levels of education, women’s (unadjusted) earnings averaged 77.1 cents for every dollar earned by men in pandemic-affected 2020, narrowing slightly from 76.7 cents in 2019. Regression results showed significant earnings differences by gender and by ethnocultural identity, adjusting for other factors. An unexplainable gender wage gap persisted in the decomposition analysis, with women earning 6% less than men due to factors that could not be explained by differences in age structure or part-time work, pointing to additional contributing but unmeasured structural dynamics. Significant earnings disparities by gender and ethnicity among chiropractors emphasize the need for equity-oriented initiatives in leadership opportunities and compensation structures, to help influence the attractiveness of the profession to new talent.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".