Factors influencing diabetes self-management among Omani patients with type-2 diabetes: Patients’ perspectives
Bibliographic record
Abstract
Background and aim: Management of diabetes requires using different approaches that include self-management in which the patients are the main key players. Diabetes self-management contributes to preventing diabetes-related complications and improving the QOL of patients with diabetes. The aim of this study was to explore the factors influencing diabetes self-management from the perspectives of the patients with type-2 diabetes in Oman.Methods: A qualitative descriptive design using semi-structured individual interviews was used to conduct this study. A purposive sample of 21 Omani patients with type-2 diabetes were interviewed. Interviews were transcribed verbatim and translated into English. Content analysis and constant comparison were used to analyze the data.Results: Eight factors influencing DSM were identified: awareness of diabetes complications and the importance of DSM, ability to adapt, support, fear of consequences, frustration and helplessness, complying with sociocultural norm/ritual, lack of care, and planning.Conclusions: To improve the quality of diabetes care and decrease the devastating diabetes-related complications, managing diabetes has to take the form of a collaborative approach that put into consideration the factors influencing diabetes self-management that were identified by this study.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 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".