The Use of Social Media by Clinical Nurse Specialists at a Tertiary Hospital: Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Recently, many health care professionals, who use social media to communicate with patients and colleagues, share information about medical research and promote public health campaigns. OBJECTIVE: This study aimed to examine the motives, barriers, and implementation of social media use among clinical nurse specialists in Oman. METHODS: A mixed methods study was conducted among 47 clinical nurse specialists at Sultan Qaboos University Hospital between November and December 2020. Qualitative data were collected using an open-ended questionnaire and analyzed using thematic analysis, and quantitative data were collected with a questionnaire and analyzed using SPSS (version 21.0; IBM Corp). RESULTS: Of the 47 clinical nurse specialists surveyed, 43 (91.5%) responded. All respondents reported using social media applications, with WhatsApp being the most commonly used platform. Most respondents (n=18, 41.9%) spent 1-2 hours per day on social media. The main motives for using social media were increasing knowledge, communication, reaching patients easily, and reducing the number of hospital visits. The main barriers to social media use were privacy concerns, time constraints, and a lack of awareness of legal guidelines for social media use in the workplace. All participants requested clear rules and regulations regarding the use of social media among health care providers in the future. CONCLUSIONS: Social media has the option to be a powerful institutional communication and health education tool for clinical nurse specialists in Oman. However, several obstacles must be addressed, including privacy concerns and the need for clear guidelines on social media use in the workplace. Our findings suggest that health care institutions and clinical nurse specialists must work together to overcome these impediments and leverage the benefits of social media for health care.Bottom of Form.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".