A Study to See the Effect of Social Media Usage Among Healthcare Providers
Bibliographic record
Abstract
Purpose This study aimed to assess how healthcare professionals (HCPs) use social media to determine how it influences the quality of patient care. Materials and methods This is a cross-sectional study conducted over eight months, between August 2020 and March 2021 using a questionnaire and checked amongst investigators. Results One hundred fifty-eight participants had electronic devices and 145 (91.9%) used social media at work. 26.6% of these HCPs said they spent less than an hour on social media forums, 31% said they spent one to two hours, 28.5% said two to three hours, and 13.9% said they spent more than four hours. As compared to nurses (46%), consultants and pharmacists use social media at a much lower rate (1% for each group). Compared to junior doctors, a higher percentage of nurses (40%) said they were aware of a social media policy at their hospital (8%). A quarter of healthcare employees (20%) were unaware of their workplace policy, potentially exposing sensitive medical details to the public. More research is needed to assess the particular effects of these results on patient care quality and can help in providing literature informing applications encrypted and secure patient data. Conclusion According to our results, a large percentage of healthcare quality professionals used social media networks. A significant proportion of doctors and nurses use it to visit online medical forums for improving education. A large portion of surveyed sample was unaware of hospital policy on social media usage. Further education is required to improve the right use of social media in the hospital setting.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".