Bibliographic record
Abstract
The evidence on evidence is conclusive. Incorporating evidence in decision-making processes can improve decision-making outcomes (Davies and Nutley 1999), but few healthcare systems have embraced the use of evidence to the extent needed to achieve the potential benefits. Throughout the world, health systems have "not delivered the desired health outcomes that are possible with current professional knowledge" (Ibrahim and Majoor 2002),. Browman, Snider and Ellis have suggested that there has been a lack of systems-level uptake of evidence-based healthcare: While systems may have access to the evidence, existing structures and processes have not facilitated effective transfer to operations.
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 | 0.203 | 0.477 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.009 | 0.066 |
| Scholarly communication | 0.033 | 0.054 |
| Open science | 0.011 | 0.025 |
| Research integrity | 0.087 | 0.136 |
| Insufficient payload (model declined to judge) | 0.014 | 0.009 |
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".