Operationalizing the Quintuple Aim of Health System Improvement Through Equity-Oriented Health Care
Bibliographic record
Abstract
Health systems in Canada and elsewhere are reeling from ongoing syndemic shocks and mounting political-economic concerns that are having significant negative impacts on health equity, and on staff recruitment, wellbeing, and retention. Pressures to privatize delivery of publicly funded healthcare services in Canada are mounting, posing an additional risk to equity in access and outcomes, especially for those least well-served by current systems. This paper examines existing approaches to health system improvement and their alignment with the quintuple aim of enhancing patient experiences and outcomes, service and system efficiency, provider well-being, and health equity. Quality improvement efforts derived from private sector models such as Lean and Six Sigma have been shown, in the Canadian context and elsewhere, to add costs and negatively impact key aims such as provider well-being and patient experiences of care, though they can improve process-specific aspects of care, especially when an integrated team approach is applied in properly resourced contexts. Models that treat equity as an add-on to Lean/Six Sigma-based approaches have not been well-tested. Equity-oriented health care (EOHC) provides a promising alternative for health system improvement aligned with the quintuple aim, and is positioned as an emerging, innovative way to mitigate mounting system pressures, enhance health service effectiveness, and improve population health.
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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 | 0.034 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".