Strategies for Addressing Barriers to Publishing Pediatric Quality Improvement Research
Bibliographic record
Abstract
BACKGROUND: Advancing the science of quality improvement (QI) requires dissemination of the results of QI. However, the results of few QI interventions reach publication. OBJECTIVE: To identify barriers to publishing results of pediatric QI research and provide practical strategies that QI researchers can use to enhance publishability of their work. METHODS: We reviewed and summarized a workshop conducted at the Pediatric Academic Societies 2007 meeting in Toronto, Ontario, Canada, on conducting and publishing QI research. We also interviewed 7 experts (QI researchers, administrators, journal editors, and health services researchers who have reviewed QI manuscripts) about common reasons that QI research fails to reach publication. We also reviewed recently published pediatric QI articles to find specific examples of tactics to enhance publishability, as identified in interviews and the workshop. RESULTS: We found barriers at all stages of the QI process, from identifying an appropriate quality issue to address to drafting the manuscript. Strategies for overcoming these barriers included collaborating with research methodologists, creating incentives to publish, choosing a study design to include a control group, increasing sample size through research networks, and choosing appropriate process and clinical quality measures. Several well-conducted, successfully published QI studies in pediatrics offer guidance to other researchers in implementing these strategies in their own work. CONCLUSION: Specific, feasible approaches can be used to improve opportunities for publication in pediatric, QI, and general medical journals.
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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 | 0.482 | 0.690 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.023 | 0.026 |
| Open science | 0.010 | 0.018 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".