CANADIAN JOURNAL OF DIABETES. 2008;32(4):281-289. Development of the SQUIRe pUblIcatIon gUIDelIneS | 281
Bibliographic record
Abstract
In 2005, we published draft guidelines for reporting studies of quality improvement interventions as the initial step in a consensus process for development of a more definitive ver-sion. The current article contains the full revised version of the guidelines, which we refer to as Standards for QUality Improvement Reporting Excellence (SQUIRE). We also describe the consensus process, which included informal feedback from authors, editors and peer reviewers who used the guidelines; formal written commentaries; input from a group of publication guideline developers; ongoing review of the literature on the epistemology of improvement and methods for evaluating complex social programs; a 2-day meeting of stakeholders for critical discussion and debate of the guidelines ’ content and wording; and commentary on sequential versions of the guidelines from an expert consul-tant group. Finally, we consider major differences between SQUIRE and the initial draft guidelines; limitations of and unresolved questions about SQUIRE; ancillary supporting documents and alternative versions that are under develop-ment; and plans for the dissemination, testing and further development of SQUIRE.
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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 | 0.045 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.022 | 0.009 |
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".