The effect of the SQUIRE (Standards of QUality Improvement Reporting Excellence) guidelines on reporting standards in the quality improvement literature: a before-and-after study
Bibliographic record
Abstract
BACKGROUND: The SQUIRE (Standards of QUality Improvement Reporting Excellence) guidelines were developed to improve the reporting of quality improvement (QI) projects. The effect of the guidelines on the completeness of reporting in the QI literature is unknown. OBJECTIVES: Our primary objective was to determine if the completeness of reporting in the QI literature has been improved[OUP_CE13] since the introduction of the SQUIRE guidelines. METHODS: We performed a before-and-after evaluation of QI articles selected from four prominent journals of healthcare quality. Twenty-five articles published in each of two time periods (2006-2008 and 2010-2011) were confirmed to be QI projects using a standardised definition and were independently evaluated by two investigators as an interim evaluation of a planned larger sample. Articles were assessed using 50 statements of the SQUIRE guidelines, and the overall change in the completeness of reporting between the two groups was determined. The value of p<0.05 was considered significant. RESULTS: Both groups were similar in characteristics. There was no significant difference in the mean (SD) number of SQUIRE statements completed by authors before and after publication of the SQUIRE guidelines, 20.2 (5.0) versus 20.4 (7.0), p=0.9. The study was stopped early due to the absence of any significant trend in the completeness of reporting. DISCUSSION: There was no overall improvement observed in the completeness of reporting of QI projects after the publication of the SQUIRE guidelines, and the study was stopped early. There is potential for improvement in reporting standards, particularly for those guideline items or statements specific to QI projects.
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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 | 0.373 | 0.554 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.012 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".