Taking triple aim at the Triple Aim
Bibliographic record
Abstract
Since its introduction to the USA, the Triple Aim is now being adopted in the healthcare systems of other advanced economies. Verma and Bhatia (2016) (V&B) argue that provincial governments in Canada now need to step up to the plate and lead on the implementation of a Triple Aim reform program here. Their proposals are wide ranging and ambitious, looking for governments to act as the “integrators” within the healthcare system, and lead the reforms. Our view is that, as a vision and set of goals for the healthcare system, the Triple Aim is all well and good, but as a pathway for system reform, as articulated by V&B, it misses the mark in at least three important respects. First, the emphasis on improvement driven by performance measurement and pay-for-performance is troubling and flies in the face of emerging evidence. Second, we know that scarcity can be recognized and managed, even in politically complex systems, and so we urge the Triple Aim proponents to embrace more fully notions of resource stewardship. Third, if we want to take seriously “population health” goals, we need to think very differently and consider broader health determinants; Triple Aim innovation targeted at healthcare systems will not deliver the goals.
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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 | 0.033 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.027 |
| Scholarly communication | 0.019 | 0.025 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.017 | 0.034 |
| 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".