Implementation of diabetes prevention programs into clinical practice and community settings: a systematic search and review
Bibliographic record
Abstract
BACKGROUND: Greater understanding of how evidence-based programs have been implemented in clinical practice and community settings is needed. Implementation science can help understand how to best implement programs, however, the fast-developing field is hindered by inconsistent terminology and reporting. To increase transparency and improve implementation science, standardized tools have been created. The aim of this systematic search and review was to identify implementation strategies, outcomes and determinants using standardized tools when diabetes prevention programs were implemented within a clinical practice and community setting. METHODS: A comprehensive peer-reviewed search strategy was used to identify relevant articles. Relevant studies were retrieved from four electronic databases and specific inclusion and exclusion criteria were applied. Implementation strategies, outcomes, determinants, and theoretical frameworks were extracted from all included articles using two standardized tools (the refined compilation of implementation strategies and the minimum dataset of implementation determinants and outcomes). Data from the extraction tool were summarized using a narrative approach. Frequency of reported implementation strategies, outcomes, determinants, and theoretical frameworks are presented. RESULTS: Retrospective researcher extraction resulted in the representation of 69 of the 73 implementation strategies. An average of 13.8 strategies (± 9.1) were reported, programs ranged from zero to 41 strategies. The most common reported strategies included: conduct educational meetings, build a coalition, and promote adaptability. Individual implementation determinants and outcomes were not extracted due to the difficulty applying standardized definitions to the dataset and the limited implementation data. Most studies (75%) lacked a theoretical framework. DISCUSSION: Significant gaps exist in reporting implementation strategies, providing sufficient detail on how implementation projects are implemented, and researching implementation variables within diabetes prevention programs. Large implementation projects contained more implementation strategies and variables than small projects. The use of standardized tools for the extraction of implementation strategies, outcomes, and determinants was difficult due to insufficient detail provided in existing literature on how programs have been implemented and ambiguity in standardized tool definitions. To build the field of implementation science, researchers must report sufficient detail on how programs have been implemented and research implementation variables. TRIAL REGISTRATION: This systematic search and review was registered on Open Science Frameworks and can be accessed with this link: https://osf.io/cbzja .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.028 | 0.117 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.021 | 0.024 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".