Plant medicine usage of people living with type 2 diabetes mellitus in Belize: A qualitative exploratory study
Bibliographic record
Abstract
BACKGROUND: Type 2 Diabetes Mellitus (T2DM) is a primary cause of death in Belize, a low-income country with the highest rates in Central and South America. As many people in Belize cannot consistently access biomedical treatment, a reality that was exacerbated by the COVID-19 pandemic, plant medicine usage is estimated to have increased in recent years. This exploratory study seeks to understand which plants are being used, patterns of usage, and the state of patient-provider communication around this phenomenon. METHODS: Implementing a Constructivist Grounded Theory qualitative design, the research team conducted 35 semi-structured interviews with adults living with T2DM, 25 informant discussions, and participant observation with field notes between February 2020 and September 2021. Data analysis followed systematized thematic coding procedures using Dedoose analytic software and iterative verification processes. RESULTS: The findings revealed that 85.7% of participants used plants in their T2DM self-management. There were three main usage patterns, namely, exclusive plant use (31.4%), complementary plant use (42.9%), and minimal plant use (11.4%), related to factors impacting pharmaceutical usage. Almost none of participants discussed their plant medicine usage with their health care providers. CONCLUSIONS: Plant species are outlined, as are patients' reasons for not disclosing usage to providers. There are implications for the advancement of understanding ethnobotanical medicine use for T2DM self-management and treatment in Belize and beyond.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".