Methodologic attributes of quality improvement studies in neonatology: a systematic survey
Bibliographic record
Abstract
Introduction Quality improvement (QI) is a growing field of inquiry in healthcare, including neonatology. However, there is limited information on the study setting, and the methodologic approaches used to develop, implement and evaluate QI interventions in neonatology studies. In this study, we describe these intervention characteristics and approaches. Methods Articles were taken from a previous publication. There, we searched MEDLINE for publications of QI studies from 2016 to 16 April 2020. We retrieved all relevant full-text publications and sampled 100 of these articles for data abstraction, stratified by the year of publication. For each QI study, we described several methodological characteristics that included: the clinical topic of QI, setting, whether the study was multicentre, stakeholder engagement, root cause analysis and related problem identification methods, implementation techniques for QI interventions, types of outcomes and statistical analysis methods used. Results We assessed 100 studies; most were conducted in the USA (56%). Academic settings and multicentre settings comprised 44% and 24% of studies, respectively. Most studies reported stakeholder engagement (81%), but infrequently reported engagement with leadership (32%) and caregivers (10%). Frequently used techniques for implementing interventions include provider education (82%), formal QI methods (42%) and audit, feedback and benchmarking (40%). Both patient-important clinical outcomes (78%) and process outcomes (89%) were frequently reported. P values were frequently reported (80%), but other statistical techniques were infrequently used. Conclusion QI studies in neonatology use diverse multicomponent interventions. Reporting of these methodologic details can be useful in designing, implementing and evaluating QI studies in clinical practice.
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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 | 0.039 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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; both teacher heads 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".