Health systems resilience in Canada: a literature review and case studies to inform strengthened resilience
Bibliographic record
Abstract
This chapter in the Elgar Handbook of Health System Resilience reviews what is known on health systems resilience in Canada, considering evidence from past pandemics and emergencies, and emerging evidence from COVID-19, and our team’s experiences. Our aim is to shed light on the strengths and challenges with Canada’s health systems to inform policy priorities to strengthen health system resilience. We adopt Thomas et al.’s definition of health systems resilience and organize our review findings according to the WHO health system building blocks. We found there has been limited explicit attention in the literature to health systems resilience, with most studies focusing on the preparedness and response stages of resilience, and most were focused on public health emergencies. The literature has uncovered some strengths, including the adaptability of health systems, particularly at local levels, a dedicated health workforce, and some improved coordination within and across health systems over time. Our review and case studies also underscored persistent challenges that require attention to strengthen health systems resilience in Canada, such as the limited health care capacity, inadequate and fragmented data systems, and lack of attention to sustaining and building public trust in government and public health authorities.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.021 | 0.044 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".