Impact of the Covid-19 Pandemic on the Doctor-Patient Relationship: A Qualitative Study in a Tertiary-Care Centre in Lebanon
Bibliographic record
Abstract
Background: The Covid-19 pandemic imposed numerous constraints on medical practice and exacerbated preexisting vulnerabilities in Lebanon’s healthcare system, which was already grappling with instability due to concurrent political and economic crises. This situation had a complex impact on the doctor-patient relationship (DPR), with both negative and positive repercussions. Methods: We conducted semi-structured interviews with physicians from various specialties practicing at a tertiary-care center in Lebanon. Our study aimed to 1) explore the impact of the Covid-19 pandemic on the DPR in Lebanon, and 2) provide recommendations for improving the DPR, healthcare policy, and education. Results: Thematic analysis of the data revealed that the pandemic had varying effects on the DPR. While both physicians and patients seemed to have developed a more favourable perception of the medical profession, communication between them appeared to be challenged by the use of personal protective equipment and patients’ concerns about close contact with physicians. The media played a vital role in educating and raising awareness during the pandemic but lacked organization and ethical standards, leading to anticipated fear and confusion among the society. Telemedicine emerged as an alternative means for communication and remote care but faced several obstacles including inadequate internet infrastructure and disruptions to physicians’ personal lives. Conclusion: Our qualitative study unveiled the multifaceted impact of the Covid-19 pandemic on perceiving the healthcare system, doctor-patient interactions, and the role of telemedicine in Lebanon, among others. These findings underscore the importance of effective communication in enhancing the DPR, the need to address misinformation on social media, and the imperative for systemic improvements to strengthen the resilience of Lebanon’s healthcare system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".