Effects of a comprehensive structured patient education intervention on disease-related knowledge and behaviour change among people living with type 2 diabetes in the Philippines
Bibliographic record
Abstract
Introduction Addressing the ongoing needs of individuals with diabetes, particularly in low- and middle-income countries like the Philippines, requires a focus on regular follow-ups with healthcare teams, adherence to healthy behaviors, and effective patient education to prevent long-term complications. The aim of this study was to ascertain the impact of a comprehensive educational program for those living with diabetes in the Philippines. Methods In a prospective study, a convenience sample of patients living with diabetes attending a cardiac rehabilitation or an outpatient diabetes clinic in the Philippines received a 12-week education intervention. Participants completed surveys at pre- and post-intervention assessing disease-related knowledge, health literacy, dietary habits, and tobacco use. Physical activity was measured by steps taken per day using wearable devices and by self-report of minutes of moderate or vigorous-intensity exercise per week. Satisfaction with the educational materials was also evaluated by a survey composed of Likert-type scale and open-ended questions. Descriptive statistics, paired t-tests or chi-square were used for data analysis. Results Overall, 184 individuals living with diabetes type 2 (mean age = 54.4 ± 12.4, 32% female) completed both assessments. There was significant improvement in disease-related knowledge (p < 0.001), daily steps measured by a wearable device and self-reported minutes of moderate/vigorous-intensity exercise (p < 0.001), and the number of fruit and vegetable servings consumed per day (p = 0.001). No significant changes were observed in health literacy levels. One participant stopped using tobacco at post-education. Educational materials were highly satisfactory to participants. Lack of time, family responsibilities, and poor internet access were the main barriers to learning reported by participants. Suggestions to improve the education provided included assessment of information needs at the start of the education, having short summaries about the topics, follow-ups post-intervention, and inviting family members to sessions. Discussion Results of this study demonstrated the positive effects a comprehensive structured patient education intervention on disease-related knowledge and behaviour changes among people living with type 2 diabetes in the Philippines.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".