Predictors of glycemic control, quality of life and diabetes self-management of patients with diabetes mellitus at a tertiary hospital in Ghana
Bibliographic record
Abstract
Abstract Background The burden of diabetes mellitus (DM) in Sub-Saharan Africa is high and continues to increase. Effective DM management focuses on key goals such as glycemic control, prevention of acute and chronic complications and improvement of quality of life (QOL). This study therefore assessed predictors of glycemic control, QOL and diabetes self-management (DSM) of patients with DM in a tertiary hospital in Ghana. Methods We conducted a cross-sectional study involving face-to-face interviews of patients with DM attending clinic using structured questionnaires and validated study instruments as well as review of medical records. A multivariable logistics regression analysis was used to identify independent factors associated with good glycemic control, poor QOL and poor DSM practices. Results The study involved 360 patients with mean age of 62.5 ± 11.6 years and a female preponderance, 271 (75.3%). The mean HbA1c among study participants was 7.8 ± 2.7% of which 44.7% had HbA1C <7%. Patients on only oral DM medications (aOR 2.14; 95% CI 1.19-3.88, p=0.012) were more likely to have good glycemic control. Urban residence (aOR 0.24; 95% CI 0.06-0.87, p=0.030) and good DSM (aOR 0.05; 95% CI 0.02-0.13, p<0.001) were protective of having poor QOL however, recent hospitalization (within the past 3 months) (aOR 4.58; 95 % CI 1.58-13.26, p=0.005) had higher odds of poor quality of life. Patients who were divorced (aOR 6.79; 95% CI 1.20-40.42, p=0.030) had higher odds of poor DSM, while having attended the clinic for more than 3 years (aOR 0.32; 95% CI 0.12-0.81, p=0.016) was protective of poor DSM. Conclusion Good social support and sustained DSM interventions result in good DSM and ultimately improves quality of life of patients with DM.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".