The need for TIDieR research translation: assessing the reporting and implementation of diabetes prevention programs through a scoping review.
Bibliographic record
Abstract
Over 400 million people worldwide have diabetes, with 90% of cases being type 2 diabetes (T2D). T2D is a chronic disease that increases the risk of heart attack, stroke, blindness, and death. Diabetes prevention programs (DPPs) have been found to be an effective method of reducing incidence of T2D. To effectively translate DPPs into communities, clear and thorough reporting of intervention characteristics is needed. This scoping review aimed to synthesize the reporting and implementation of DPPs. PRISMA guidelines for conducting scoping reviews were followed, and Medline, CINAHL, PsycINFO, EMBASE and SPORTDiscus were searched for studies related to T2D prevention and DPP interventions. Articles that reported a diet and/or exercise intervention being delivered to adults “at risk” for T2D were included in the review. 25,110 articles were screened, of which 351 met inclusion criteria. The Template for Intervention Description and Replication (TIDieR) checklist was used to score DPPs on thoroughness of reporting. DPPs were scored in 13 different categories on a scale from 0 (did not report) to 2 (thoroughly reported) with a total out of 26. Mean TIDieR score for DPPs was 15.7, ranging between 7 and 25. As rates of T2D increase, there is a need for implementation of effective DPPs into communities. On average, DPPs only report on 60% of TIDieR domains, which can make translation and implementation of evidence-based DPPs into new communities difficult. More thorough reporting of DPP intervention characteristics is needed to improve the implementation of DPPs from research into clinical and community 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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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; a candidate call from one teacher head, 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".