Voices of those living with type 2 diabetes in Belize: barriers to care before and during the COVID-19 pandemic
Bibliographic record
Abstract
Belize has the highest national prevalence of type 2 diabetes (T2D) of Central and South America, and fifth direst in the world. T2D is the leading cause of death in Belize, a country facing burdens of increasing prevalence with few resources. Since March of 2020, the COVID-19 pandemic has exacerbated the difficulties of those living with T2D in Belize. To address T2D issues in Belize, our interdisciplinary research team explored the barriers to care and self-management for adult patients with T2D in Belize prior to and during the COVID-19 pandemic.Research relationships between Canadian (ARH) and Belizean (LE) authors have been ongoing since 2016. Together we used a qualitative Constructivist Grounded Theory design generating knowledge through 35 semi-structured patient interviews, 25 key informant discussions, and participant observation with field notes between February 2020 to September 2021. We used Dedoose analysis software for a systematized thematic coding process, as well as iterative verification activities. Findings revealed several barriers to care and self-management, including: 1) the tiered health and social care system with major gaps in coverage; 2) the unfulfilled demand for accurate health information and innovative dissemination methods; and 3) the compounding of loss of community supports, physical exercise, and health services due to COVID-19 restrictions. In the post-pandemic period, it is necessary to invest in physical, nutritional, economic, and psychosocial health through organized activities adaptable to changeable public health restrictions. Recommendations for activities include sending patients informational and motivational text messages, providing recipes with accessibly sourced T2D foods, televising educational workshops, making online tools more accessible, and mobilising community and peer support networks.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.010 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".