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Record W4414752088 · doi:10.1186/s12913-025-13471-5

Prevalence and characteristics of medical and rehabilitation utilization among Canadians with arthritis from 2001 to 2018: a cross-sectional population-based study

2025· article· en· W4414752088 on OpenAlexafffundabout
Sheilah Hogg‐Johnson, Dan Wang, Jessica J. Wong, Silvano Mior, Pierre Côté

Bibliographic record

VenueBMC Health Services Research · 2025
Typearticle
Languageen
FieldMedicine
TopicRheumatoid Arthritis Research and Therapies
Canadian institutionsWestern UniversityOntario Tech UniversityPublic Health OntarioCanadian Memorial Chiropractic CollegeUniversity of Toronto
FundersCanadian Institutes of Health ResearchHealth CanadaOntario Chiropractic Association
KeywordsRehabilitationPsychological interventionHealth careHealth administrationPublic healthArthritisNursing researchPoisson regression

Abstract

fetched live from OpenAlex

BACKGROUND: Arthritis covers a range of chronic diseases presenting as joint pain and inflammation with prevalence of 20% in Canadians. Treatment guidelines for arthritis depend upon the type of arthritis but most include recommendations for rehabilitation interventions designed to optimize functioning and reduce disability. We set out to estimate the prevalence of healthcare utilization with different providers and to explore factors associated with utilization of different providers among Canadians with arthritis. METHODS: This population-based study used Canadian Community Health Survey data (2001–2018) restricted to respondents with arthritis (≥12 years). We used self-reported consultation with healthcare providers (medical doctor, chiropractor, physiotherapist, nurse, psychologist) (2001–2010), and self-reported regular healthcare provider (2015–2018). We calculated the 12-month prevalence of utilization with providers, and used modified Poisson regression to assess predisposing (e.g. age, sex, education), enabling (e.g. income, province) and need (e.g. self-percieved health) factors associated with utilization of providers. RESULTS: From 2001–2010 and 2015–2018, respectively, prevalence of utilization of medical doctors was 92.0% (95%CI: 91.7–92.2%) and 91.0% (95%CI: 90.5–91.5%); chiropractors 13.1% (12.8–13.4%) and 9.6% (9.1–10.1%); physiotherapists 14.5% (14.1–14.8%) and 9.4% (8.9–9.9%); nurses 14.2% (13.9–14.5%) and 7.5% (7.2–7.9%); psychologists 3.0% (2.8–3.1%) and 3.9% (3.5–4.2%). Females were more likely to see any provider. Users of chiropractic care were less likely to be smokers and more physically active with greater utilization in the western provinces than in the east. Those with poorer self-perceived health were more likely to see physiotherapists, nurses and psychologists. Consultation with a nurse (2001–2010) was more likely in the northern territories, while regular care from a nurse (2015–2018) was more likely in older age groups. CONCLUSIONS: Canadians with arthritis were most likely to see medical doctors. Characteristics of healthcare utilizers varied by provider type. Geographical variation in utilization of chiropractors and physiotherapists likely related to differences by province and over time in what provincial health insurance covered while geographical variation in utilization of nurses was likely related to the lack of availability of medical doctors. Findings inform the need to strengthen healthcare delivery for Canadians, perhaps providing better access to providers of rehabilitation interventions.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.106

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.006
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.033
GPT teacher head0.410
Teacher spread0.377 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes3
Has abstractyes

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