Tip 2 Diyabetli Bireylerde Problem Çözme Eğitiminin Öz Yönetim Davranışlarına Etkisi
Bibliographic record
Abstract
Individuals with type 2 diabetes need to develop healthy lifestyle habits for blood glucose control and individuals should maintain regular self-management behaviors However, the individuals have problems to participated in the life and maintenance of these behaviors. Problem-solving education support individuals to get healthy habits. This study was performed to investigate the effects of the problem-solving training applied with meaningful activities for individuals activity performance, routines and habits, problem-solving style, psychosocial self-efficacy, well-being, and emotional stress areas in type 2 diabates individuals. This study included 67 individuals volunteers of type 2 diabetes in total. Participants were divided into randomization method intervention (n = 33) and control (n = 34) group. The Physical and Sociodemographic Characteristics, activity performance problems with Canadian Occupational Performance Measure, typical days with Time Geography Diary styles to cope with problems with Brief COPE, psychosocial self-efficacy with Diabetes Empowerment Scale, well-being with World Health Organization-5 Well Being İndex, information on the areas of emotional stress with Problem Areas in Diabetes Questionnaire were recorded for each participant. The intervention is scheduled as a total of 6 modules, with 6 weeks and at least 1. session per week. 1, 2 and 6. modules has the standard programme, modules 3, 4 and 5. are arranged to the needs of the individual. The intervention and control group were evaluated before, after, and after intervention for 3 months and the data obtained were compared with each other. The results show that individuals with type 2 diabetes increased their typical day-to-day diabetes self-management activity after the intervention, in addition to the problematic areas of performance, problems with coping style, psychosocial self-efficacy and well-being (p<0,05). There was no difference in the areas of emotional stress between the groups with progress for both groups. It has been found that diabetic individuals encounter different problems when adapted diabetes self-management activities and maintenance of life, and these are influenced self-management behaviors of the individual. For this reason, programs that diabetic persons are supported with occupational therapy points of view are needed to adapt healthy therapeutic behaviors to routine and habits.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".