mHealth use for non-communicable diseases care in primary health: patients’ perspective from rural settings and refugee camps
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: Non-communicable diseases (NCDs) account for 85% of deaths in Lebanon and contribute to remarkable morbidity and mortality among refugees and underserved populations. This study assesses the perspectives of individuals with hypertension and/or diabetes in rural areas and Palestinian refugee camps towards a population based mHealth intervention called 'eSahha'. Methods: The study employs a mixed-methods design to evaluate the effectiveness of SMSs on self-reported perceptions of lifestyle modifications. Quantitative data was collected through phone surveys, and qualitative data through focus group discussions. Descriptive statistics and bivariate analysis were performed. Results: About 93.9% (n = 1000) of respondents perceived the SMSs as useful and easy to read and understand. About 76.9% reported compliance with SMSs through daily behavioral modifications. Women (P = 0.007), people aged ≥76 years (P < 0.001), unemployed individuals (P < 0.001), individuals who only read and write (P < 0.001) or those who are illiterate (P < 0.001) were significantly more likely to receive and not read the SMSs. Behavior change across settings was statistically significant (P < 0.001). Conclusion: While SMS-based interventions targeting individuals with hypertension and/or diabetes were generally satisfactory among those living in rural areas and Palestinian refugee camps in Lebanon, a more tailored approach for older, illiterate and unemployed individuals is needed. Keywords: e-health, refugees.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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