Towards a Model of Stewardship and Accountability in Support of Innovation and “Good” Failure
Bibliographic record
Abstract
From an evolutionary perspective, failures of imagination and missed opportunities to learn from experimentation are as potentially harmful for the health system as failures of practice. The conundrum is encapsulated in the fact that while commentators are steadfast about the need on the part of the stewards of the health system to avoid any waste of public dollars, they are also insistent about the need for innovation. There is tension between these two imperatives that is often unrecognized: the pursuit of efficiency, narrowly defined, can crowd out the goal of innovation by insisting on the elimination of "good waste" (the costs of experimentation) as well as "bad waste" (the costs of inefficiency) (Potts 2009). This tension is mirrored in the two broad drivers of performance reporting in health systems: public accountability and quality improvement. Health organizations, predominantly funded by public funds, are necessarily accountable for the ways in which those funds are used and outcomes achieved. This paper reviews how accountability relationships should be re-examined to create room for "good failure" and to ensure that system accountability does not become a barrier to performance improvement.
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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 | 0.052 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.102 |
| Scholarly communication | 0.030 | 0.055 |
| Open science | 0.007 | 0.020 |
| Research integrity | 0.105 | 0.097 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".