Self-care management among patients with type 2 diabetes in East Jerusalem
Bibliographic record
Abstract
Objective: Little research exists on diabetes self-care management (DSCM) in Arab populations. We examined the contribution of health belief constructs, socioeconomic position (SEP) and clinical factors (glycated haemoglobin [HbA1C] level, type of diabetes treatments, and receiving professional guidance) to DSCM among Arab patients in East Jerusalem with type 2 diabetes. Method: Using a structured questionnaire, we conducted face-to-face interviews with a random sample of 230 patients with type 2 diabetes in a large diabetes clinic. DSCM included engagement in any of the following in the last week: physical activity, consumption of low-fat and low-sugar diet, self-monitoring of blood glucose, medication uptake and foot care. We obtained HbA1C levels from the clinic’s patient registry. We used linear regression to examine the contribution of health beliefs, SEP and clinical factors to explaining DSCM. Results: Adherence to DSCM was low. Most patients (84.8%) were physically inactive, 64.3% did not consume a low-fat or low-sugar diet (46.5%) and 51% did not self-monitor blood glucose. However, medication adherence (95.7%) and foot care were high (77.4%). About 71% of participants had high HbA1C (>7.0%). In the multivariate analysis, total DSCM scores were higher among patients with low financial barriers, high perception of the benefits of DSCM and higher self-efficacy. Patients using oral medication (vs insulin) had significantly lower DSCM scores. Conclusion: Among Arab patients with diabetes, more interventions are needed to encourage DSCM, specifically in areas of lifestyle (diet and physical activity). Patients’ financial barriers, benefits of DSCM and patient self-efficacy should be emphasised.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".