Determinants of Poor Glycemic Control Among Type 2 Diabetes Patients: A Systematic Review
Bibliographic record
Abstract
Poor glycemic control remains a pervasive challenge in type 2 diabetes mellitus (T2DM) management, contributing to elevated risks of complications and healthcare burdens globally. This systematic review aimed to synthesize evidence on the determinants of poor glycemic control (hemoglobin A1C (HbA1c) >7%) among adults with T2DM. We followed PRISMA guidelines to search for relevant studies across five different databases, where we found 239 studies. First, the studies were screened for duplicates and then assessed for eligibility by screening through titles, abstracts, and ultimately full text. Upon carefully assessing for eligibility using inclusion and exclusion criteria, only 12 studies were found relevant and were included in this systematic review. The review found that most studies had a moderate risk of bias based on the Newcastle-Ottawa Scale, with only three rated as low risk. Key factors linked to poor glycemic control included low socioeconomic status, medication non-adherence, longer diabetes duration, obesity, insulin-based regimens, and limited access to healthcare. Insulin use, in particular, was paradoxically associated with worse control due to its complexity and adherence challenges, especially in low-resource settings. Regional differences highlighted unique barriers like cultural practices in Ethiopia and gaps in diabetes education in Eritrea. These findings reflect the complex and context-specific nature of glycemic control, especially in low- and middle-income countries. The review calls for simplified treatments, affordable medications, and better management of comorbidities, while encouraging future research using longitudinal and mixed-methods approaches to guide more effective, patient-centered interventions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".