Choosing and accessing COVID-19 treatment options: a qualitative study with patients, caregivers, and health care providers in Lebanon
Bibliographic record
Abstract
BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has strained healthcare systems globally, particularly in terms of access to medicines. Lebanon has been greatly affected by the pandemic, having faced concomitant financial and economic crises. The objective of the study was to understand the experiences of patients with COVID-19 in Lebanon, as well as those of their families, and healthcare providers, with regards to their treatment decisions and accessibility to COVID-19 medicines. METHODS: For this qualitative study, we conducted 28 semi-structured interviews. We used purposive sampling to recruit participants with a diverse range of perspectives. The data collection phase spanned from August to November 2021 and was conducted virtually. After transcribing and translating the interviews, we employed thematic analysis to identify recurring themes and patterns. RESULTS: In total, 28 individuals participated in this study. Participants highlighted challenges owing to the COVID-19 pandemic and economic crisis. Accessing COVID-19 medicines posed major hurdles for physicians and patients, given limited availability, global shortages, local circumstances, community hoarding and stockpiling by pharmacies. Providers based treatment decisions on research, local and international practice guidelines, experiences and expert feedback. Patients sought information from social media, community members and physicians, as well as through word of mouth. Accessing medicines involved navigating the healthcare system, the black market, charities, personal networks and political parties and sourcing from abroad. The medicines were either free, subsidized or at inflated costs. CONCLUSIONS: This study highlights the diversity and complexity of factors influencing decision-making and accessing medicines during the COVID-19 pandemic in Lebanon. Future research should explore strategies for ensuring medicine access during crises, drawing insights from comparative studies across different countries.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | medium |
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".