Integrating Health Technologies in Health Services for Syrian Refugees in Lebanon: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Lebanon currently hosts around one million Syrian refugees. There has been an increasing interest in integrating eHealth and mHealth technologies into the provision of primary health care to refugees and Lebanese citizens. OBJECTIVE: We aimed to gain a deeper understanding of the potential for technology integration in primary health care provision in the context of the protracted Syrian refugee crisis in Lebanon. METHODS: A total of 17 face-to-face semistructured interviews were conducted with key informants (n=8) and health care providers (n=9) involved in the provision of health care to the Syrian refugee population in Lebanon. Interviews were audio recorded and directly translated and transcribed from Arabic to English. Thematic analysis was conducted. RESULTS: Study participants indicated that varying resources, primarily time and the availability of technologies at primary health care centers, were the main challenges for integrating technologies for the provision of health care services for refugees. This challenge is compounded by refugees being viewed by participants as a mobile population thus making primary health care centers less willing to invest in refugee health technologies. Lastly, participant views regarding the health and technology literacies of refugees varied and that was considered to be a challenge that needs to be addressed for the successful integration of refugee health technologies. CONCLUSIONS: Our findings indicate that in the context of integrating technology into the provision of health care for refugees in a low or middle income country such as Lebanon, some barriers for technology integration related to the availability of resources are similar to those found elsewhere. However, we identified participant views of refugees' health and technology literacies to be a challenge specific to the context of this refugee crisis. These challenges need to be addressed when considering refugee health technologies. This could be done by increasing the visibility of refugee capabilities and configuring refugee health technologies so that they may create spaces in which refugees are empowered within the health care system and can work toward debunking the views discovered in this study.
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How this classification was reachedexpand
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 | 0.024 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".