Self-management practices among diabetic patients during the COVID 19 pandemic: Systematic review
Bibliographic record
Abstract
Introduction: Self-management is crucial in diabetes care and assessing its impact during the COVID-19 pandemic is vital. This systematic review examines the pandemic's influence on self-management practices among diabetic patients. Methods: We conducted searches in PubMed, Cochrane Library, and Science Direct from December 2019 to June 2021. Included studies evaluated the effect of COVID-19 on self-management practices in diabetic patients. After removing duplicates, two independent reviewers conducted title and abstract reviews, followed by full-text screening, in accordance with PRISMA guidelines. Results: Of 1083 records, nine studies were included, comprising 5279 patients with sample sizes ranging from 52 to 1510. During the pandemic, most self-management practices remained relatively stable. Improvements were observed in dietary control (1% to 82.6%), self-blood glucose monitoring (11.3% to 47.1%), medication adherence (8.1% to 18.4%), weight management (19% to 40.9%), and physical activity (1% to 25.7%). Notably, physical activity showed a decrease (19% to 69.1%) during the pandemic. Conclusion: Compared to the pre-pandemic period, dietary control exhibited the most significant improvement, while physical activity demonstrated the least increase. Medication adherence and self-blood glucose monitoring were relatively better than other self-management practices. It is imperative to enhance self-management practices in diabetic patients during similar pandemics.
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 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".