Exploring diabetic foot screening programs with integrated consolidated framework for implementation: Rapid review protocol
Bibliographic record
Abstract
<ns3:p> Background Diabetic foot ulcers (DFU)s pose significant challenges for individuals with diabetes, leading to severe consequences, such as lower extremity amputations (LEA)s, reduced quality of life, and increased mortality. Disorganized diabetic foot care services contribute to health inequities worldwide, highlighting the need for structured preventive measures, which require an understanding of organizational and systemic components of the implementation of foot screening programs or initiatives, including equity factors. Thus, the <ns3:italic>Consolidated Framework for Implementation Research</ns3:italic> (CFIR) is one of the most widely used frameworks for assessing these factors and contexts. This helps to reduce the risk of failure of implementation efforts in the real world and can help to support the scaling up of preventative measures. This review aims to analyze foot screening programs or initiatives for individuals at risk of DFUs and LEAs, define their key components and implementation determinants, identify barriers and facilitators, and describe effective implementation strategies in primary care with CFIR. Methods A rapid review will be conducted following the Canadian method by Dobbins (2017) and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocol guidelines. The research question is defined using the PICO framework. A systematic search will be conducted in MEDLINE, CINAHL, and EMBASE. Primary studies in English or French, including both primary study designs and knowledge syntheses, will be screened according to the defined eligibility criteria via Covidence. Study quality will be appraised using the Mixed Methods Appraisal Tool and data will be synthesized guided by the CFIR. Data synthesis will focus on implementation determinants, including barriers, facilitators, and implementation strategies. Discussion Findings will inform policy, practice and decision making regarding the implementation of screening programs. This can promote the development of screening programs for diabetic foot complications across Canada or in other countries. </ns3:p>
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".