Characteristics and Healthcare Utilization of Canadians Living With Participation and Activity Limitations (2001-2010): A Population-Based Cross-Sectional Study
Bibliographic record
Abstract
INTRODUCTION: Individuals with participation and activity limitations face important healthcare challenges. OBJECTIVES: We investigated healthcare utilization and characteristics of Canadians living with participation and activity limitations between 2001 and 2010. METHODS: We pooled data from 5 cycles of the Canadian Community Health Survey (2001-2010 CCHS). The multistage stratified cluster-sampling method used covered approximately 98% of Canadians, aged 12 years and older residing in private dwellings. We described sociodemographic, behavioral, and health-related characteristics of participants with participation and activity limitations and reported their annual utilization (prevalence; 95% CI) of 7 healthcare providers. Multivariable modified Poisson regression identified individual characteristics associated with healthcare utilization and examined the trends over time. RESULTS: Annually, 8.1 million Canadians aged 12 years and older (29.8%) reported participation and activity limitations. Most common health conditions were back problems (37%) and arthritis (34%). Predominant healthcare providers were medical doctors (88.8%; 95% CI = 88.6-89.0), nurses (16.3%; 95% CI = 16.1-16.6), physiotherapists (15.0%; 95% CI = 14.7-15.2), and chiropractors (14.4%; 95% CI = 14.2-14.7). Overall, males, older adults, immigrants, those with lower education, lower income, recent employment, and better general health were less likely to consult providers. Over time, utilization of most non-medical providers increased. CONCLUSION: Participation and activity limitations are prevalent in Canada, and most consulted medical doctors. Disadvantaged groups reported lower utilization of most providers, emphasizing access challenges and the need for equitable and integrated healthcare policies. Improving access to rehabilitation services and their inclusion within universal healthcare coverage should be a priority.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".