Healthcare utilisation among Canadian adults in rural and urban areas – The Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
OBJECTIVE: The objective is to determine the use of health-care services (physician visits, emergency department use and hospitalisations) in rural areas and examine differences in four geographic areas on a rural to urban spectrum. METHODS: We conducted a secondary analysis of cross-sectional data from a population-based prospective cohort study, the Canadian Longitudinal Study on Aging (CLSA). Participants included community-dwelling adults aged 45-85 years old from the tracking cohort of the CLSA (n = 21,241). Rurality was classified based on definitions from the CLSA sampling frame and similar to the 2006 census. Main outcome measures included self-reported family physician and specialist visits, emergency department visits and hospitalisations within the previous 12 months. Results were compared for four geographic areas on a rural-urban continuum. Univariate and bivariate analyses were performed on data from the 'tracking cohort' of the CLSA, Chi-square tests were used for categorical variables. Logistic regression models were created for the main outcome measures. RESULTS: Participants in rural and mixed rural and urban areas were less likely to have seen a family physician or a specialist physician compared to urban areas. Those living in rural and peri-urban areas were more likely to visit an emergency department compared to urban areas. These differences persisted after adjusting for sociodemographic and health-related variables. There were no significant rural-urban differences in hospitalisations. CONCLUSION: Rural-urban differences were found in visits to family physicians, specialists and emergency departments.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".