A cross-sectional assessment of diabetes self-management, education and support needs of Syrian refugee patients living with diabetes in Bekaa Valley Lebanon
Bibliographic record
Abstract
BACKGROUND: Patients with diabetes require knowledge and skills to self-manage their disease, a challenging aspect of treatment that is difficult to address in humanitarian settings. Due to the lack of literature and experience regarding diabetes self-management, education and support (DSMES) in refugee populations, Medecins Sans Frontieres (MSF) undertook a DSMES survey in a cohort of diabetes patients seen in their primary health care program in Lebanon. METHODS: Structured interviews were conducted with diabetes patients in three primary care clinics between January and February 2015. Scores (0-10) were calculated to measure diabetes core knowledge in each patient (the DSMES score). Awareness of long-term complications and educational preferences were also assessed. Analyses were conducted using Stata software, version 14.1 (StataCorp). Simple and multiple linear regression models were used to determine associations between various patient factors and the DSMES Score. RESULTS: A total of 292 patients were surveyed. Of these, 92% had type 2 diabetes and most (70%) had been diagnosed prior to the Syrian conflict. The mean DSMES score was 6/10. Having secondary education, previous diabetes education, a 'diabetes confidant', and insulin use were each associated with a higher DSMES Score. Lower scores were significantly more likely to be seen in participants with increasing age and in patients who were diagnosed during the Syrian conflict. Long-term complications of diabetes most commonly known by patients were vision related complications (68% of patients), foot ulcers (39%), and kidney failure (38%). When asked about the previous Ramadan, 56% of patients stated that they undertook a full fast, including patients with type 1 diabetes. Individual and group lessons were preferred by more patients than written, SMS, telephone or internet-based educational delivery models. CONCLUSIONS: DSMES should be patient and context appropriate. The variety and complexities of humanitarian settings provide particular challenges to its appropriate provision. Understanding patient baseline DSMES levels and needs provides a useful basis for humanitarian organizations seeking to provide diabetes care.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".