Addressing Healthcare Waiting Time Challenges in Canada: Insights From Emerging Initiatives
Bibliographic record
Abstract
Canada's public healthcare system faces persistent challenges with waiting times. Prolonged delays lead to adverse physical and mental health outcomes, higher treatment costs, and economic burdens for patients and families. This editorial examines the drivers of extended wait times and policy responses at both provincial and federal levels. Contributing factors include systemic features of the Canadian healthcare system, such as shared federal-provincial jurisdiction, along with staffing shortages, population aging, structural inefficiencies, and poorly integrated health information technology. Provinces have introduced strategies such as digital health solutions, capacity expansion, workforce innovations (including Physician Assistants [PAs]), and expanded scopes of practice for pharmacists. At the federal level, a 10-year $196.1 billion investment announced in 2023 is supporting these initiatives. While such measures indicate progress, wait times remain a significant concern. Achieving equitable and timely access will require coordinated and sustained strategies that address systemic challenges and deliver long-term improvements.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.017 | 0.005 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.006 |
| 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".