An Embedded Systems Perspective in Conceptualizing Canada’s Healthcare Sustainability
Bibliographic record
Abstract
Healthcare sustainability has been dominated by a strong fiscal orientation. In an era of budget cuts and staff reductions, the financial challenges in Canadian healthcare are immediate and must be addressed. However, an independent focus on financial viability is too narrow a framing; too limited to allow for the kind of creative, novel, and even radical thinking that is required to fundamentally alter the current course of healthcare in Canada and internationally. Prospects for solving the current financial challenges are likely to be greatly enhanced if we simultaneously account for the broad and interrelated dimensions of sustainability. What would a healthcare system look like if sustainability were adopted as the focal and principal goal? And what might a “deep” sustainability orientation imply for how we think about and manage healthcare systems? This analysis is informed by the notion that healthcare systems are fully contained within the societal system, which is itself fully contained within the broader ecological system. This model, which foregrounds nature as the most fundamental and important system, has both greater ecological validity and particular relevance to the healthcare context given the interdependence between the health of natural systems and the health of humans. Our understanding of nature in relation to health may be key to solving or at least reducing the economic burden of healthcare. A multidimensional systems orientation thus has the potential to unveil new modes of thinking that highlight intersectoral relations, communications, collaboration, and cross-boundary learning for improved health and wellbeing, healthcare performance, and sustainability.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.009 | 0.022 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".