Lessons learned in the provision NCD primary care to Syrian refugee and host communities in Lebanon: the need to ‘act locally and think globally’
Bibliographic record
Abstract
BACKGROUND: Prevention and control of non-communicable diseases (NCDs) remain inadequate in resource-scarce countries, particularly in conflict situations. This paper describes a multicomponent intervention for management of hypertension and diabetes among older adult Syrian refugees and the Lebanese host community and reflects on challenges for scaling up NCD integration into primary care in humanitarian situations. METHODS: Using a mixed method approach, the study focused on monitoring and evaluation of the three components of the intervention: healthcare physical facilities and documentation processes, provider knowledge and guideline-concordant performance, and refugee and host community awareness. RESULTS: Findings revealed overall high compliance of healthcare workers with completing data collection forms. Their knowledge of basic aspects of hypertension/diabetes management was adequate, but diagnosis knowledge was low. Patients and healthcare providers voiced satisfaction with the program. Yet, interruptions in medicines' supplies and lapses in care were perceived by all study groups alike as the most problematic aspect of the program. CONCLUSIONS: Our intervention program was aligned with internationally agreed-upon practices, yet, our experiences in the field point to the need for more 'local testing' of modified interventions within such contexts. This can then inform 'thinking globally' on guidelines for the delivery of NCD care in crisis settings.
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| 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".