Bibliographic record
Abstract
This paper presents several hypotheses about why public reporting of performance information in healthcare has not had more impact. Abstracting information from paper-based systems is slow and expensive, impairing the ability to provide timely, accurate information about system performance. Alternatively, the incentives that drive the behaviour of participants in the system may obstruct meaningful change in response to performance information. The delivery of healthcare is a very complex enterprise. This makes creating a set of indicators that is both useful and readily understandable by the general public very difficult. Perhaps the industry should consider the development of composite indicators not unlike those used to report on the macroeconomic performance of regional economies. In this regard, the development of composite indicators for quality of care (using measures of evidence-based protocol adherence) and access (through wait-times measures) is suggested. In conclusion, the paper states that it is too early to judge the efficacy of public reporting of performance information in healthcare; much more development of performance reporting is required.
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 | 0.426 | 0.769 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.015 | 0.021 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.028 | 0.030 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".