SQUIRE-EDU (Standards for QUality Improvement Reporting Excellence in Education): Publication Guidelines for Educational Improvement
Bibliographic record
Abstract
The SQUIRE 2.0 (Standards for QUality Improvement Reporting Excellence) guidelines were published in 2015 to increase the completeness, precision, and transparency of published reports about efforts to improve the safety, value, and quality of health care. The principles and methods applied in work to improve health care are often applied in educational improvement as well. In 2016, a group was convened to develop an extension to SQUIRE that would meet the needs of the education community. This article describes the development of the SQUIRE-EDU extension over a three-year period and its key components. SQUIRE-EDU was developed using an international, interprofessional advisory group and face-to-face meeting to draft initial guidelines; pilot testing of a draft version with nine authors; and further revisions from the advisory panel with a public comment period. SQUIRE-EDU emphasizes three key components that define what is necessary in systematic efforts to improve the quality and value of health professions education. These are a description of the local educational gap; consideration of the impacts of educational improvement to patients, families, communities, and the health care system; and the fidelity of the iterations of the intervention. SQUIRE-EDU is intended for the many and complex range of methods used to improve education and education systems. These guidelines are projected to increase and standardize the sharing and spread of iterative innovations that have the potential to advance pedagogy and occur in specific contexts in health professions education.
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 | 0.550 | 0.699 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.024 | 0.033 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.023 | 0.013 |
| Open science | 0.010 | 0.019 |
| Research integrity | 0.014 | 0.023 |
| Insufficient payload (model declined to judge) | 0.011 | 0.018 |
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".