Bibliographic record
Abstract
Since the 1990s, health experts have warned Canadian federal and provincial governments that the growing older population would increase pressure on the healthcare system. This study examines the state of older adults (aged 65 and over) before the COVID-19 pandemic to determine whether they faced more significant challenges in accessing healthcare services than younger adults. Microdata from the 2017–2018 Canadian Community Health Survey (CCHS) was analyzed using chi-square tests and crosstabulations for this research. The findings indicate that older adults report better health outcomes and greater access to healthcare services than their younger counterparts. However, it is crucial to note a significant limitation of the CCHS. It only collects data from individuals in community settings and excludes those in institutionalized settings. This exclusion is important because older adults in long-term care facilities typically experience poorer health and may face more substantial access barriers. As a result, a vital segment of the older population is missing from this analysis. The results might also reflect processes related to younger adults who need to find primary care physicians for the first time and/or the challenges that younger adults face as they move from one labour market to another. Further research is needed to thoroughly examine the health and accessibility challenges faced by all younger and older adult residents in Canada.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".