Exploring diabetes status and social determinants of health influencing diabetes-related complications in a Northwestern community, Ontario, Canada: A mixed method study protocol
Bibliographic record
Abstract
Diabetes is a common chronic condition affecting the many spheres of individuals' lives. It can also lead to severe complications without continuous management. Accordingly, this paper describes a study protocol aimed at 1) determining the status and prevalence of diabetes complications in a Northwestern Ontario community; 2) exploring the internal (e.g., demographic and clinical variables) and external factors (e.g., access to services and resources) affecting diabetes outcomes (e.g., complications, emergency room visits, hospitalizations); 3) critically exploring how the social determinants of health affect self-management for individuals living with diabetes; and 4) identifying individuals' needs, concerns, and challenges to monitor and regulate diabetes. The study uses a cross-sectional design and a social constructivist approach based on qualitative data collection. The proposed study will include patients with type 1 and type 2 diabetes with or without diabetes complications who have been attending the Centre for Complex Diabetes Care (CCDC) in Thunder Bay, Ontario, Canada, since January 2019. Quantitative data related to diabetes complications and other outcomes, diabetes management, and demographic and clinical status will be retrieved from patients' charts using a data extraction form. Analyses of the quantitative data will include the prevalence of diabetes complications, rate of hospitalizations, and their associations with diabetes management, access to services, and social determinants of health. Additionally, interviews will occur with at least 10 participants with or without diabetes complications to understand their needs, concerns, and struggle to self-manage diabetes daily. The results of this study will generate evidence to support future research and policy on the development and implementation of an educational program to improve self-care management and outcomes for individuals living with diabetes and its complications in Northwestern Ontario.
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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 | 0.015 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.013 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".