mHealth use for non-communicable diseases care in primary health: patients’ perspective from rural settings and refugee camps
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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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".