The chiropractic workforce: a global review
Bibliographic record
Abstract
Background: The world is faced with a chronic shortage of health workers, and the World Health Organization (WHO) has estimated a global shortage of 7.2 million health workers resulting in large gaps in service provision for people with disability. The magnitude of the unmet needs, especially within musculoskeletal conditions, is not well established as global data on health work resources are scarce. Methods: (scope of practice and legal rights). An electronic survey was issued to contact persons of constituent member associations of the World Federation of Chiropractic (WFC). In addition, data were collected from government websites, personal communication and internet searches. Data were analysed using descriptive statistics. Worldwide density maps of the distribution of numbers of chiropractors and providers of chiropractic education were graphically presented. Results: Information was available from 90 countries in which at least one chiropractor was present. The total number of chiropractors worldwide was 103,469. The number of chiropractors per country ranged from 1 to 77,000 (median = 10; IQR = [4-113]). Chiropractic education was offered in 48 institutions in 19 countries. Direct access to chiropractic services was available in 81 (90%) countries, and services were partially or fully covered by government and/or private health schemes in 46 (51.1%) countries. The practice of chiropractic was legally recognized in 68 (75.6%) of the 90 countries. It was explicitly illegal in 12 (13.3%) countries. Conclusion: We have provided information about the global chiropractic workforce. The profession is represented in 90 countries, but the distribution of chiropractors and chiropractic educational institutions, and governing legislations and regulations largely favour high-income countries. There is a large under-representation in low- and middle-income countries in terms of provision of services, education and legislative and regulatory frameworks, and the available data from these countries are limited.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".