Diversity in the Chiropractic Profession: Preparing for 2050
Bibliographic record
Abstract
As the diversity of the United States (US) population continues to change, concerns about minority health and health disparities grow. Health professions must evolve to meet the needs of the population. The purpose of this editorial is to review current trends in the diversity of chiropractic students, faculty, and practitioners in the United States. This editorial was informed by a search of the literature, to include PubMed, using the terms chiropractic and diversity, minority, and cultural competency. Demographic information for the chiropractic profession was obtained from the National Board of Chiropractic Examiners and The Chronicle of Higher Education. These data were compared to diversity data for medical doctors and the national and state populations from the American Association of Medical Colleges and the US Census, respectively. Surprisingly little has been published in the peer-reviewed literature on the topic of diversity in the chiropractic profession. For the variables available (sex and race), the data show that proportions in the US chiropractic profession do not match the population. State comparisons to associated chiropractic colleges show similar relationships. No reliable data were found on other diversity characteristics, such as gender identity, religion, and socioeconomic status. The chiropractic profession in the United States currently does not represent the national population with regard to sex and race. Leaders in the profession should develop a strategy to better meet the changing demographics of the US population. More attention to recruiting and retaining students, such as underrepresented minorities and women, and establishing improved cultural competency is needed.
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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".