Prevalence and practice characteristics of urban and rural or remote Australian chiropractors: Analysis of a nationally representative sample of 1830 chiropractors
Bibliographic record
Abstract
OBJECTIVE: To determine the prevalence and clinical management characteristics of chiropractors practising in urban and rural or remote Australia. DESIGN: A cross-sectional analysis of the Australian Chiropractic Research Network project data. SETTING: Nationally representative sample of registered chiropractors practising in Australia. PARTICIPANTS: Chiropractors who participated in the Australian Chiropractic Research Network project and answered a question about practising in urban or rural or remote areas in the practitioner questionnaire. MAIN OUTCOME MEASURE: The demographics, practice characteristics and clinical management of chiropractors. RESULTS: The majority of chiropractors indicated that they practise in urban areas only, while 22.8% (n = 435) practice in rural or remote areas only and 4.0% (n = 77) practice in both urban and rural or remote areas. Statistically significant predictors of chiropractors who practice in rural or remote areas, as compared to urban areas, included more patient visits per week, practising in more than one location, no imaging facilities on site, often treating degenerative spinal conditions or migraine, often treating people aged over 65 years, frequently treating Aboriginal and Torres Strait Islander people and frequently using biomechanical pelvic blocking or the sacro-occipital technique. CONCLUSION: A substantial number of chiropractors practice in rural or remote Australia and these rural or remote-based chiropractors are more likely to treat a wide range of musculoskeletal cases and include an Indigenously diverse group of patients than their urban-located colleagues. Unique practice challenges for rural or remote chiropractors include a higher workload and a lack of diagnostic tools. Chiropractors should be acknowledged and considered within rural or remote health care policy and service provision.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 teacher head, 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".