Identifying Factors Which Enhance the Self-Management of Type 2 Diabetes: A Systematic Review with Thematic Analysis
Bibliographic record
Abstract
BACKGROUND: Individuals with type 2 diabetes play a pivotal role in their health. Enhancing the self-management of diabetes can improve blood glucose control, and quality of life, and reduce diabetes-related complications. We have identified factors influencing the self-management of type 2 diabetes to inform strategies that may be applied in the long-term management of blood glucose control. METHODS: We conducted a systematic literature review of recent studies published between January 2010 to December 2020 to identify the available evidence on effective self-management strategies for type 2 diabetes. The databases used for the searchers were Scopus, PubMed, Science Direct, CINAHL, and Google Scholar. We assessed English language publications only. The screening of titles was duplicated by two researchers. We then conducted a thematic analysis of the key findings from eligible publications to identify reoccurring messages that may augment or abate self-management strategies. RESULTS: We identified 49 relevant publications involving 90,857 participants. Four key themes were identified from these publications: Individual drive, social capital, Knowledge base, and Insufficient health care. High motivation and self-efficacy enabled greater self-management. The importance of family, friends, and the health care professional was salient, as were the negative effects of stigma and labelling. Enablers to good self-management were the level of support provided and its affordability. Finally, the accessibility and adequacy of the health care services emerged as fundamental to permit diabetes self-management. CONCLUSIONS: Self-management of type 2 diabetes is an essential strategy given its global presence and impact, and the current resource constraints in health care. Individuals with type 2 diabetes should be empowered and supported to self-manage. This includes awareness raising on their role in self-health, engaging broader support networks, and the pivotal role of health care professionals to inform and support. Further research is needed into the capacity assessment of healthcare systems in diabetes medicine, targeted low-cost resources for self-management, and the financial requirements that enable self-management advice to be enacted.
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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 | 0.042 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.028 | 0.025 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.005 |
| 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".