Digital professionalism on social media: The opinions of undergraduate nursing students
Bibliographic record
Abstract
BACKGROUND: Social media are a suite of popular online technologies that enable people to share and co-create digital content. Evidence suggests some nursing students utilise social media inappropriately but there is limited literature on nursing students' opinions of professionalism in online environments. This study aimed to examine the opinions of nursing students in relation to digital professionalism on social media. METHOD: A descriptive, cross-sectional study was conducted with undergraduate nursing students in the United Kingdom (n = 112). An existing self-reported questionnaire was adapted for data collection. This was distributed to adult nursing students enrolled across all four years of a Bachelor of Nursing programme. Data were analysed using descriptive statistics. FINDINGS: Many nursing students were heavy social media users (n = 49, 44%), with Facebook, Instagram, and Snapchat being the most popular applications. Nursing students were also aware of the professional nursing regulator, the Nursing and Midwifery Council, guidelines on responsible social media use (n = 48, 43%). Nursing students' responses to various digitally professional scenarios revealed agreement that posts about alcohol or sexually explicit content, along with comments about colleagues or patients were inappropriate. However, there were mixed views around taking photographs at work, with some nursing students across all four years of the degree programme perceiving this to be satisfactory behaviour. DISCUSSION: The opinions of nursing students towards digital professionalism on social media are somewhat aligned with professional standards, although students can hold varying views on the subject. More research on how nursing students employ social media is warranted to ensure their opinions match their actual practice in online environments. It is also recommended to educate nursing students about the professional values and behaviours required on social media and how best to communicate, interact, and share information on the various online platforms, to minimise personal and organisational risk.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".