Care, Attention and Making Tough Choices: Learning from Failure Means Weeding the Garden
Bibliographic record
Abstract
Like ecosystems in nature, healthcare systems are complex, interdependent systems. Does that mean they should be left to the forces of slower, evolutionary transformation? Given the pressures and expectations on public healthcare, the more deliberate hand of a gardener may be needed to "weed out" programs that are no longer fruitful in solving current issues. Taking such a discerning view of health system programs would mean re-examining attitudes about failure. Just as harvested plants provide compost that feeds the garden, knowledge gained from failure can strengthen healthcare. A public environment that favours "success stories" means other system participants don't get the benefit of the knowledge that can be gleaned from failure. Scientific disciplines advance knowledge by studying failed experiments. Health leaders must do the same. Even programs deemed "success stories" have likely failed in some ways, experience that should be shared as transparently and widely as the successes. Both notable successes and valiant failures can be equally valuable in designing health system programs that meet the needs of all patients.
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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.057 | 0.066 |
| 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".