Strengthening health care in Canada post-COVID-19 pandemic
Bibliographic record
Abstract
The purpose of this policy briefing is to examine our health care systems through the lens of the COVID-19 pandemic and identify how we can strengthen health care in Canada post-pandemic. The COVID-19 pandemic has provided compelling evidence that substantive changes to our health care systems are needed. Specifically, the pandemic has emphasized structural inequities on a broad scale within Canadian society. These include systemic racial and socioeconomic inequities that must be addressed broadly, including in the delivery of health care. We make recommendations about what we can do to emerge stronger from the pandemic. While these recommendations are not novel, how they are framed and contextualized differs because of the problems in our health care system that have been highlighted and exacerbated by the pandemic.The evidence is clear that socioeconomic circumstances, intergenerational trauma, adverse early life experiences, and educational opportunities are critical factors when it comes to health over the life course. Given the problems in the delivery of health care that the pandemic has revealed, we need a different approach. How health care was organized prior to the COVID-19 pandemic did not produce what people wanted and needed in terms of health care and outcomes. How do we emerge from COVID-19 with an effective, equitable, and resilient health care system for all Canadians? To address health inequities and emerge from the pandemic with strengthened health care in Canada, we must consider how Amartya Sen's capabilities framework on social well-being can be operationalized to achieve better health care and health outcomes. Specifically, we address the need to: strengthen primary care and improve access to primary care; utilize a community-embedded approach to care; and implement better integration across the care continuum, including integration between primary care and public health. Coherent governance and leadership that are charged with realizing benefits through collaboration will maximize outcomes and promote sustainability. Only when we provide access to high-quality culturally competent care that is centered around the individual and their needs will we be able to make true headway in addressing these long-standing health inequities.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.027 | 0.007 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".