#FAIL: Defining, Understanding and Owning our Failures
Bibliographic record
Abstract
Despite our best efforts to reform the healthcare system, significant challenges remain. To some extent, our progress is being hampered because of our hesitation to learn from past mistakes. In his article "Systematically Identified Failure Is the Route to a Successful Health System", Zwarenstein (2015) argues that we must right this wrong and begin to systematically identify, acknowledge and learn from failure if we want to make true progress. This commentary outlines some key steps that must be taken to help us move past failure and apply lessons to future healthcare reforms. To achieve this end state we propose adopting a disciplined approach that includes clearly defining policy goals, stratifying failures into categories to help facilitate learning and encouraging leaders to acknowledge and learn from failure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.031 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.034 |
| Scholarly communication | 0.015 | 0.026 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.061 | 0.080 |
| Insufficient payload (model declined to judge) | 0.005 | 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".