Mapping the components of the effective implementation of diabetes prevention programmes after gestational diabetes mellitus: a protocol for a scoping review
Bibliographic record
Abstract
INTRODUCTION: Women with a history of gestational diabetes mellitus (GDM) have a high lifetime risk of developing type 2 diabetes. Diabetes prevention programmes may reduce this risk. However, challenges related to the successful implementation of diabetes prevention programmes after GDM exist. Our objective is to map the components of the effective implementation of diabetes prevention programmes after GDM. We also plan to connect the available evidence on the effective implementation of diabetes prevention programmes to the Consolidated Framework for Implementation Research. METHODS AND ANALYSIS: We will conduct a scoping review following Levac's adaptation of Arksey and O'Malley's framework for scoping reviews. We will report it according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews. Using a peer-reviewed search strategy, we will search Medline, Embase, PsycInfo and Emcare for primary studies describing the effective implementation of diabetes prevention programmes after GDM. Study selection will be completed in DistillerSR by two independent reviewers. Data will be extracted by one reviewer and verified by a second reviewer for accuracy using data extraction forms in DistillerSR. ETHICS AND DISSEMINATION: Ethics approval was not required. Study results will be published in a peer-reviewed journal and presented at relevant conferences. STUDY REGISTRATION DETAILS: This scoping review protocol was registered with Open Science Framework (OSF; preregistration, 15 April 2024; registration ID: 10.17605/OSF.IO/MPNQD).
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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 | 0.169 | 0.170 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.011 | 0.014 |
| Bibliometrics | 0.022 | 0.021 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.060 | 0.018 |
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".