The DOI matches its Crossref record.
Unprofessional Content Posted Online Among Nursing Students
Abstract
This study investigated the posting of unprofessional content online among nursing students. Surveys of 293 schools of nursing revealed that 77% had encountered at least 1 incident of students posting such content. Respondents reported greatest concerns about content pertaining to educational and professional affiliations and to patient confidentiality. Most schools are developing responses to online unprofessionalism. Nursing education must stay current with social networking technologies and students' educational needs.
Overview
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In the citing paper
“Breaches of patient privacy was a concern for many different types of HCPs [34,43,47,57,91,94,97,107,111,119].”
From Dangers and Benefits of Social Media on E-Professionalism of Health Care Professionals: Scoping Review (2021) · cites this paper as [111]
In the citing paper
“Unprofessional behavior on SM of HCPs can have legal consequences, potentially affecting credibility and licensure.”
From Dangers and Benefits of Social Media on E-Professionalism of Health Care Professionals: Scoping Review (2021) · cites this paper as [111]
In the citing paper
“Compromising confidentiality concerns were described in numerous studies in this review [34, 43, 47, 57, 91, 94, 97, 107, 111, 119], especially about breaches of patient privacy or possible risks of violating HIPAA online.”
From Dangers and Benefits of Social Media on E-Professionalism of Health Care Professionals: Scoping Review (2021) · cites this paper as [111]
Citations
Cited by
Sorted by citations
Vukušić Rukavina, T., Viskić, J., Machala Poplašen, L., Relić, D., Marelić, M., Jokic, D., & Sedak, K. (2021) · Journal of Medical Internet Research · doi:10.2196/25770 · Cited by 199
“Breaches of patient privacy was a concern for many different types of HCPs [34,43,47,57,91,94,97,107,111,119].”
InfluentialSocial media in nurse education: Utilization and E-professionalism
Duke, V. J. A., Anstey, A., Carter, S., Gosse, N., Hutchens, K. M., & Marsh, J. A. (2017) · Nurse Education Today · doi:10.1016/j.nedt.2017.06.009 · Cited by 113
Guraya, S. S., Guraya, S. Y., & Yusoff, M. S. B. (2021) · BMC Medical Education · doi:10.1186/s12909-021-02802-9 · Cited by 86
Dennen, V. P., Choi, H., & Word, K. (2020) · Educational Technology Research and Development · doi:10.1007/s11423-020-09796-z · Cited by 79
Medical student use of Facebook to support preparation for anatomy assessments
Pickering, J. D., & Bickerdike, S. (2016) · Anatomical Sciences Education · doi:10.1002/ase.1663 · Cited by 78
Metadata
- Authors
- Marnocha, S., Marnocha, M. R., & Pilliow, T.
- Affiliations
- Milwaukee VA Medical Center (US)
- Corresponding author
- Marnocha, Suzanne
- Marnocha, Mark R.
- ORCID
- Marnocha, Suzanne
- Venue
- Nurse Educator
- Published
- 2015-05
- Volume
- 40
- Issue
- 3
- Pages
- 119-123
- ISSN
- 0363-3624 (ISSN-L)
- OpenAlex
- W2316327410
- Cited by
- 45
- References
- 0
- Citations by year
- 2025: 2 · 2024: 5 · 2023: 3 · 2022: 4 · 2021: 6 · 2020: 5 · 2019: 7 · 2018: 1 · 2017: 8 · 2016: 4
- FWCI
- 5.11
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Nurse Educator (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Health › Social Media in Health Education
- Health Sciences › Nursing › Leadership and Management › Healthcare Education and Workforce Issues
- Social Sciences › Sociology and Political Science › Impact of Technology on Adolescents
- Keywords
- nursing students, online posts, social networking, professional conduct, patient confidentiality, professional affiliation, nursing schools, institutional responses, nursing education
- MeSH terms
- Confidentiality, Data Collection, Ethics, Nursing, Faculty, Nursing, Female, Humans, Interprofessional Relations, Male, Middle Aged, Schools, Nursing, Students, Nursing, United States, Internet, Social Networking
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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