The doctor will not see you now: investigating the social determinants of specialist care using the Canadian Longitudinal Study on Aging (CLSA)
Bibliographic record
Abstract
Background: The Canada Health Act mandates universal access to medical services for all Canadians. Despite this, there are significant disparities in access based on socioeconomic status, race and ethnicity, immigrant status, and indigeneity. However, there is limited evidence on the use of specialist services among older adults in Canada. The primary objective of this study is to identify the associations of social determinants of health with access to medical specialist services for Canadians aged 45 years and older. The second objective is to identify the reasons for not being able to access the needed specialist care. Methods: A cross-sectional analysis of the Canadian Longitudinal Study on Aging survey was conducted. Based on the Andersen's model of health services use, a multivariable logistic regression model was used to evaluate the associations between 'not being able to access the needed specialist service(s) in the last 12 months' and individual-level sociodemographic determinants. Results: Approximately 97% of those who required specialist care in the last year were able to visit a specialist. Of the participants who were not able to access the needed specialist services, about half (50.90%) were still waiting for a visit. The following factors were associated with greater difficulty in accessing specialist care: being younger (45-54 years), living in a rural area, having some post-secondary education, having a household income below $50,000 a year, not having a family physician, and having fair or poor perceived general health. Residents of British Columbia and Nova Scotia had a higher likelihood of reporting difficulty compared to those residing in Ontario. Conclusion: While a majority of respondents were able to access specialist services when needed, those who had difficulty in accessing care were more likely to come from socially marginalized groups. Targeted policy interventions and improved health system coordination can reduce these barriers to care.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".