Physician Roles and Responsibilities in the Context of a Pandemic in Resource-Limited Areas: Impact of Social Media
Bibliographic record
Abstract
Objective: This article aims to explore the role and responsibility of physicians in the era of social media; the authors take as an example of the current pandemic of Coronavirus disease 2019 (COVID-19). Also, we highlight how social media impact the way populations trust and follow the recommendations of the governments. Methods: We identified relevant articles to date using a manual library search, journal publications on the subject, and critically reviewed them. Results: We critically examined the fake news around COVID-19 disease: the disease origin, manifestations, symptoms, and treatments. The authors also explore the high expectation of people and changes in behaviors that led to risky manners, including self-medication after American President Donald Trump has claimed a major benefit of treatment with chloroquine in COVID-19. Surprisingly, the potential BCG vaccination trials in the COVID-19 pandemic were also greeted with much controversy and rejection, especially in Africa. This paper ends with some advice to various stakeholders, including leaders of global health national health organizations, and physicians on the measures to be taken in case of a similar situation in the future. Conclusions: Social media offer significant benefits for individual and public health promotion, especially when used wisely and prudently. They equally provide opportunities for advancement and professional development. However, any careless use of such platforms poses a formidable danger to health care practitioners. Lately, there are existing guidelines issued by health care organizations and professional societies which provide sound and useful principles that health care practitioner should follow to avoid pitfalls. The authors also end by stressing the importance of culturally adapting prevention messages in the context of such a pandemic.
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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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".