The profile of older adults seeking chiropractic care: a secondary analysis
Bibliographic record
Abstract
BACKGROUND: Musculoskeletal conditions are the primary reason older adults seek general medical care, resulting in older adults as the highest consumers of health care services. While there is high use of chiropractic care by older adults, there is no recent, specific data on why older adults seek chiropractic care and how chiropractors manage conditions. Therefore, the purpose of this study was to describe the demographic characteristics of older adults seeking chiropractic care, and to report problems diagnosed by chiropractors and the treatment provided to older adults who seek chiropractic care. METHODS: tests or t-tests, accounting for the clustering of patients and encounters within chiropractors. RESULTS: A total of 6781 chiropractor-adult patient encounters were recorded. Of these, 1067 encounters were for persons aged > 65 years (16%), from 897 unique older patients. The most common diagnosis within older adult encounters was a back problem (56%), followed by neck problems (10%). Soft tissue techniques were most frequently used for older patients (85 in every 100 encounters) and in 29 of every 100 encounters, chiropractors recommended exercise to older patients as a part of their treatment. CONCLUSIONS: From 6781 chiropractor-adult patient encounters across two countries, one in seven adult chiropractic patients were > 65 years. Of these, nearly 60% presented with a back problem, with neck pain and lower limb problems the next most common presentation to chiropractors. Musculoskeletal conditions have a significant burden in terms of disability in older adults and are the most commonly treated conditions in chiropractic practice. Future research should explore the clinical course of back pain in older patients seeking chiropractic care and compare the provision of care to older adults across healthcare professions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".