Treatment satisfaction of diabetic patients: what are the contributing factors?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Treatment satisfaction is an important factor of quality of care, especially in treating chronic diseases such as diabetes mellitus. Identifying factors that independently influence treatment satisfaction may help in improving clinical outcomes. OBJECTIVE: To find the relationship between treatment satisfaction of diabetic patients and socio-demographic, clinical, adherence, treatment and health perception factors. METHODS: Patients were interviewed by telephone about their socio-demographic parameters, health status, clinical data and treatment factors. The Diabetes Treatment Satisfaction Questionnaire (DTSQ) was used to measure satisfaction and adherence. This is a cross-sectional study, as part of a larger study of chronic patients in Israel. Subjects were randomly selected diabetes patients. The main outcome measures were DTSQ levels. A multivariate linear regression model was constructed to identify factors independently associated with patients' satisfaction. RESULTS: In all, 630 patients were included in the study. Multivariate analysis indicated that demographic parameters (e.g. female gender, P = 0.036), treatment factors (e.g. type of medication, P < 0.001), adherence factors (e.g. difficulty attending follow-up or taking medications, P < 0.001) and clinical factors (e.g. diabetes complications, P < 0.01) were independently associated with lower treatment satisfaction. CONCLUSIONS: Treatment satisfaction is lower among diabetic patients who have a lower educational level, who are insulin treated or have a diabetic complication and is related to difficulties in taking medications and coming to follow-up visits. Addressing the specific needs of these patients might be effective in improving their satisfaction, thus having a positive influence on other clinical outcomes.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it