Bibliographic record
Abstract
Practice audits are useful opportunities to improve practice efficiency and effectiveness, reduce clinical errors, demonstrate quality care to stakeholders, promote high standards of practice, lower the risk of liability, and foster practice change. However, a benefit that is usually overlooked is the possibility of publication of the results of a practice audit. Publication (research) has a number of benefits for the clinician, including skill development as a scholar, communicator, professional, and collaborator. A practice audit is beneficial to an individual physician; furthermore, publication of the audit results could be beneficial for many others such as health care providers, patients, and other stakeholders in a health care system. The problem is that practice audits often begin without a clear plan. The important steps in planning and carrying out a practice audit can be captured by thinking about how a research publication evolves. Thus, a good researcher is a good practice auditor. This paper reviews the author's experience and provides examples and directions of the process of practice-audit-publish.
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 | 0.365 | 0.482 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.015 | 0.024 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.013 | 0.025 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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".