Mandatory Public Reporting: Build It and Who Will Come?
Bibliographic record
Abstract
Rates of healthcare-associated infections (HAI) are being reported on an increasing number of public information websites in response to legislative mandates driven by consumer advocacy. This represents a new strategy to advance patient safety and quality of care by informing a broad audience about the relative performance of individual healthcare facilities. Unlike typical consumer health informatics products, the target audience and targeted health behaviors are less easily defined; further, the impact on providers to improve care is unknown relative to other incentives to improve. To address critical knowledge gaps facing all state agencies embarking on this new frontier, we found it essential and straightforward to recruit the assistance of university research faculty from a variety of disciplines. That interdisciplinary group was quickly able to define a 5-year applied evaluation research agenda spanning a progressive set of crucial questions.
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 | 0.210 | 0.236 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.021 | 0.037 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 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; 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".