Untangling the association between the burden of chronic back problems, current utilization of chiropractic care, and availability of chiropractors at the health region level: an ecological study protocol.
Bibliographic record
Abstract
Background: Chronic back problems (cBP) are the leading cause of disability in Canada, with chiropractors as second most-consulted professionals for cBP care. However, little is known about chiropractor supply, demand, and gaps between them. We will determine the prevalence of cBP, chiropractic utilization, the chiropractic availability across Canadian health regions; compute a demand-supply measure; and investigate characteristics associated with the demand-supply. Methods: We designed an ecological study with 102 Canadian health regions as the unit of analysis. We will estimate derived and observed demand using the Canadian Community Health Survey data (2015/2016, 2021/2022), and supply using Canadian Chiropractic Association membership data (2021/2022). We will use spatial analyses to map the prevalence of cBP (derived demand), chiropractic utilization for cBP (observed demand), and chiropractor availability (supply) across health regions. Poisson regression models will assess the population factors associated with supply and demand-supply disparities. Conclusion: The identification of geographical disparities in chiropractic care and the exploration of contextual factors associated with demand-supply dynamics may inform healthcare planning and resource allocation for the management of chronic back problems in Canada.
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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 | 0.021 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".