Shining light on Vitamin C Deficiency and Scurvy in Canada: A Scoping Review Protocol of Risk Profiles, Health Outcomes, and Interventions
Bibliographic record
Abstract
Abstract Scurvy, resulting from vitamin C deficiency, is linked to serious health outcomes, including impaired collagen synthesis, anemia, and delayed wound healing. Once considered largely eradicated in high-income countries, scurvy has re-emerged among specific Canadian populations, driven by factors such as inadequate dietary intake, socioeconomic disparities, and limited access to nutritious foods. Despite growing awareness, evidence regarding its prevalence, risk factors, and public health responses in Canada remains sparse and fragmented. To address this knowledge gap, this study protocol outlines a scoping review designed to: (a) assess the prevalence and incidence of scurvy and vitamin C deficiency in Canada; (b) identify at-risk populations and contributing factors; (c) describe associated health outcomes; and (d) map existing nutritional interventions and public health strategies for prevention and management. Our review will follow the Joanna Briggs Institute (JBI) Manual for Evidence Synthesis (Chapter 11: Scoping review) and the Arksey and O’Malley methodological framework. Keywords will be identified and used to develop search strings used for a comprehensive search of various databases. The review will adhere to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. Our study selection process will systematically screen titles/abstracts and full texts of potentially relevant articles to ensure a comprehensive analysis through thematic analysis. We will implement a clearly defined extraction process to gather the most pertinent articles, maximizing the quality and impact of our research. Ethical approval is not required because this study will review publicly available data and will not involve human participants. Our scoping review will synthesize evidence on scurvy and vitamin C deficiency in Canada, identifying knowledge gaps, contributing factors such as food insecurity, and vulnerable populations. Findings will inform research priorities, guide public health policies, and support targeted interventions to prevent deficiency and enhance nutritional health for Canadians.
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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 | 0.146 | 0.136 |
| Meta-epidemiology (narrow) | 0.005 | 0.007 |
| Meta-epidemiology (broad) | 0.013 | 0.013 |
| Bibliometrics | 0.022 | 0.021 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.066 | 0.014 |
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".