Assess Nurses' Social Media Conduct's Effect on Patient Trust
Bibliographic record
Abstract
Background: The increasing reliance on social media has transformed how healthcare professionals, including nurses, access and share medical knowledge. Digital platforms such as WhatsApp, Facebook, Twitter, and LinkedIn provide avenues for professional networking, information exchange, and patient education. However, challenges such as misinformation, privacy concerns, and ethical dilemmas complicate the use of these tools in clinical practice. This study explores the role of social media in nursing, examining its benefits, risks, and the types of health information sought by nurses. Methods: A mixed-method, cross-sectional study design was employed, integrating quantitative and qualitative approaches. Data were collected from 280 nurses through structured questionnaires and focus group discussions (FGDs). The survey assessed demographic characteristics, social media usage patterns, and perceptions of its advantages and challenges. Quantitative data were analyzed using SPSS, while qualitative insights were derived through thematic analysis using NVivo software. Results: Findings indicate that WhatsApp, Facebook, and Twitter are the most frequently used platforms for accessing health information. Nurses primarily sought information on patient experiences, health conditions, and second opinions, while topics such as insurance, medication, and therapy details received less attention. Key benefits included increased access to medical knowledge, enhanced professional networking, and emotional support. However, challenges such as misinformation (44.1%), privacy concerns (55.5%), information overload (29.5%), and risks of personal data disclosure (31.3%) were identified as major concerns. Conclusion: The study highlights the significant impact of social media in nursing, providing an essential tool for professional development and patient engagement. However, risks such as misinformation and ethical concerns necessitate guidelines to ensure responsible usage. It is recommended that healthcare institutions implement policies to promote digital literacy and safeguard privacy while maximizing the benefits of social media in nursing practice.
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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 | 0.020 | 0.145 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".