The Link Between Difficulty in Accessing Health Care and Health Status in a Canadian Context
Bibliographic record
Abstract
Much of the Canadian population reports some level of difficulty in accessing health care services. Despite being a recognized determinant of health, the relationship between access to health care and overall health has not been examined extensively. This study is an analysis of the Canadian Community Health Survey 2016 database. A composite score for difficulty in accessing health care was constructed based on several survey questions. Self-rated health (SRH), the measure of general health status, was compared between individuals with and without difficulty in accessing health care services by estimating prevalence rate ratios adjusting for age, sex, education, income, urban/rural status, race, and Indigenous status. After adjustment for pertinent confounders, difficulty in accessing health care was not statistically significantly associated with SRH. However, in stratified models, difficulty accessing health care was associated with a 12% lower probability of reporting good SRH among non-white individuals. Test of interactions for other social determinants was not significant. For racial minorities, inequalities in access to health care are associated with lower self-rated health. Further research to investigate causes underlying difficulties in accessing health care could lead to public health programs ensuring all Canadians receive equal health care services.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".