Provincial variations in not having a regular medical doctor and having unmet healthcare needs among Canadians
Bibliographic record
Abstract
BACKGROUND: Despite spending 11.0% of the total gross domestic product, the quality of healthcare services in Canada has received mixed reviews. We first separately examined provincial variations in not having a regular medical doctor and having unmet healthcare needs among Canadians. Second, we evaluated provincial variations in the impact of not having a regular medical doctor on having unmet healthcare needs among Canadians. METHODS: We applied logistic regressions using data from the 2014 and 2017-2018 Canadian Community Health Surveys (CCHS). The total sample size for this study was 120,345 individuals aged 12 years and older: 61,240 from the 2014 CCHS and 59,105 from the 2017-2018 CCHS. RESULTS: We found significant provincial variations in not having a regular medical doctor and having unmet healthcare needs among Canadians. People in Quebec and the Territories were more likely not to have a regular medical doctor than their peers in Alberta. People in Quebec and the Territories were also more likely to have unmet healthcare needs than their counterparts in Alberta. Not having a regular medical doctor impacted whether Canadians reported having unmet healthcare needs to varying degrees across provinces. CONCLUSION: Findings from this study may contribute to designing province-specific policy interventions and inform efforts that seek to address barriers to having a regular medical doctor and reducing unmet healthcare needs among Canadians.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".