The role of social support and the built environment on diabetes management among structurally exposed populations in three regions in Ghana
Bibliographic record
Abstract
Abstract Sub-Saharan Africa is undergoing an epidemiological transition driven by rapid, unprecedented demographic, socio-cultural, and economic transitions. These transitions are driving increases in the risk and prevalence of diabetes and other non-communicable diseases (NCDs). As NCDs rise, several attempts have been made to understand the individual level factors that increase NCDs risks, knowledge, and attitudes around specific NCDs as well as how people live and manage NCDs. While these studies are important, and enhance knowledge on chronic diseases, little attention has been given to the role of social and cultural environment in managing chronic NCDs in underserved settings. Using purposive sampling among persons living with Diabetes Mellitus (PLWD) and participating in diabetes programs from regional and municipal hospitals in the three underserved regions in Ghana ( n = 522), we assessed diabetes management and supportive care needs of PLWDs using linear latent and mixed models (gllamm) with binomial and a logit(log) link function. The result indicates that PLWDs with strong perceived social support (OR = 2.27, p ≤ 0.05) were more likely to report good diabetes management compared to PLWDs with weak perceived social support. The built environment, living with other health conditions, household wealth, ethnicity and age were associated with diabetes management. Overall, the study contributes to wider discussions on the role changing built and socio-cultural environments in the rise of diet-related diseases and their management as many Low- and Middle-Income Countries (LMICs) experience rapid epidemiological and nutrition transitions.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".