Reversing the Stigma around Canada’s Poor-Performing Healthcare Systems
Bibliographic record
Abstract
R ecent statistics report that healthcare spending growth is persistently high; in recent years, spending growth exceeded 5% (CIHI 2022).Some portion of the outsized growth can be attributed to the COVID-19 pandemic, though the cause is irrelevant.High spending growth in the healthcare sector is not a good prospect for taxpayers or for education and social programs competing for the same pot of money.Spending is important, but it is not the only attribute for measuring the success of our provinces' and territories' efforts to fund healthcare services that maintain or improve their populations' health.Access to care and the quality of that care are important indicators of performance.Unfortunately, for Canadians, performance on these indicators is also not highly regarded.The oft-cited Commonwealth Fund data recently noted that Canada has the dubious ranking of the tenth lowest-performing health system of the 11 countries examined (Schneider et al. 2021).To borrow a hockey catchphrase, how do governments' health systems get out of the penalty box and make the substantial reforms needed to stop performing so poorly?In my opinion, governments need an aggressive multi-pronged strategy to catch up with the performance of other countries' health systems.Canadians should not continue to accept slow and incremental gains through low-intensity policies or "value veneers" (Batniji and Shrank 2023; Pandey et al. 2023).A well-funded strategy for improving health system performance would apply policies in a number of key areas simultaneously to address underlying factors (Drummond et al. 2023). Social CareTo improve health system performance, reforms must expand outside healthcare.Reforms should include provincial and territorial social care systems that affect their residents' health and well-being.Not addressing residents' social determinants of health, such as poverty or homelessness, will perpetuate clogged emergency departments and overreliance on acute care.
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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 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.035 | 0.045 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".