Bibliographic record
Abstract
Despite the mixed results of Brown and colleagues' review of the evidence for the use of performance indicators in health policy, this paper argues that they have an important place. Healthcare organizations cannot rely on altruism alone to motivate improved performance. Berwick, supporting the use of performance indicators in healthcare, argues "threats to survival are necessary to build will for improvement." He argues that the job of managers is to create organizations where such threats are clearly perceived, but balanced against a culture of "safety" in which individuals can learn and improve the care provided. This step in the causal chain gets insufficient attention from the "KAB+" evaluation model that Brown and colleagues employ. The use of performance indicators in publicly funded healthcare systems goes beyond arming the "consumer" of healthcare with relevant information; it is a fundamental question of democratic accountability. Methods for evaluating the evidence base of public policy must take into account the contextual (economic, social and political) factors that support or impede the achievement of policy objectives.
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 | 0.091 | 0.298 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.031 |
| Scholarly communication | 0.016 | 0.033 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.075 | 0.071 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".