A Scoping Review Protocol of Food Bank Based Intervention Aimed at Chronic Disease Prevention and or Management
Bibliographic record
Abstract
ABSTRACT Background In Canada, chronic diseases such as type 2 diabetes (T2D) and cardiovascular disease (CVD), are leading causes of death and disability. Food insecurity elevates a persons risk of developing chronic diseases, making food banks increasingly popular locations for chronic disease prevention and management programs. Objectives The aims of this scoping review are to 1) identify and summarize the body of literature on food bank based chronic disease prevention and/or management initiatives; 2) to evaluate the impact of the interventions on chronic disease prevention and/or management; and 3) identify current evidence gaps. Methods and Analysis The following protocol discusses the methods for conducting a scoping review of food bank based intervention for chronic disease prevention and/or management. The review will follow the Joanna Briggs Institute’s (JBI) methodology for scoping reviews and reported in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). The following bibliographic databases will be searched: Medline (Ovid), CINAHL, Embase, and ProQuest. All extracted studies will be independently screened by two reviewers, with any disagreements being resolved via a third reviewer. Results will be presented through tabular and graphical visualizations, accompanied with narrative explanations.
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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 | 0.156 | 0.151 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.015 | 0.012 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.115 | 0.022 |
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".