Systematically Identified Failure Is the Route to a Successful Health System
Bibliographic record
Abstract
Although we have a systematic approach to innovation and evaluation (and scale-up) for treatments, medical technologies and diagnostic tests in healthcare, we have no equivalent for service delivery innovations. Service delivery innovation is common but frequently goes unevaluated, leading to less systematic decisions about which innovations are scaled up and which ones are not. The absence of a formal evaluation system for service delivery innovation means that there is no objective standard for evaluating an innovation's success or failure, and thus no way to decide whether it should be scaled up, adapted and retested, or not scaled up at all. This results in "bad failure" - the scale-up of innovations that are untested, and the failure to scale-up other innovations that might have been effective but no one measured their effectiveness in a systematic way.
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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 | 0.100 | 0.253 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".