The SQUIRE Guidelines: A Scholarly Approach to Quality Improvement
Bibliographic record
Abstract
Quality improvement (QI) work resonates with physicians and trainees, as it is immediately relevant to their work. Yet the academic medical community has largely avoided QI work, because it is perceived as time consuming, lacking scientific merit, and adversely impacting academic advancement. The Accreditation Council for Graduate Medical Education's Clinical Learning Environment Review program's focus on quality has prompted more physicians to do QI work, but often without the needed skill sets, which results in poorly conceived and ultimately unsuccessful improvement initiatives. Since this renders the work unpublishable, it further impedes progress in the field of health care improvement and widens the quality chasm.1 Academic physicians skilled in QI processes are crucial to the transformation of health care delivery. Along with trainees, they represent an untapped resource and are important players in addressing organizational quality problems.2The 2008 Standards for Quality Improvement Reporting Excellence (SQUIRE) guidelines sought to provide a structured approach to the reporting of, and lend legitimacy to, the scholarly dissemination of QI initiatives. In 2015, SQUIRE 2.0 was released as an update to these guidelines3 to minimize redundancies and offer a standardized framework for reporting and planning QI work.4The guidelines are broken down into 4 main sections: (1) Why did you start?; (2) What did you do?; (3) What did you find?; and (4) What does it mean? Each section is divided into subsections (18 total), which together delineate the key elements of a QI report. SQUIRE 2.0 more explicitly emphasizes the importance of articulating a rationale for proposed changes and describing the role of context, allowing authors and readers to determine the generalizability of this QI approach to other settings. Awareness and use of SQUIRE 2.0 guidelines can support academic physicians' and trainees' ability to complete rigorously conducted QI initiatives. The guidelines may facilitate greater engagement in and recognition for QI work in the academic environment.QI work may appear daunting. However, using the SQUIRE 2.0 guidelines as a starting point, interested faculty members or program directors can take simple steps to engage colleagues and trainees.Long-term efforts should be focused on building capacity for QI work in academic settings, and include investing in infrastructure at an institutional level and ensuring trained, incentivized faculty.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".