CoChat
Sign Up
Cited by 28Social Media in Health EducationEducational Technology in LearningInnovations in Medical Education

Social Media in Health Science Education: An International Survey

Open access
O'Sullivan, Elizabeth, Cutts, Emily, Kavikondala, Sushma, Salcedo, Alejandra, D'Souza, Karan, Hernandez-Torre, Martin, Anderson, Claire, Tiwari, Agnes +2 more·2017·JMIR Medical Education
DOI 10.2196/mededu.6304PMC PMC5244031
Open this paper in CoChat — free account

Abstract

BACKGROUND: Social media is an asset that higher education students can use for an array of purposes. Studies have shown the merits of social media use in educational settings; however, its adoption in health science education has been slow, and the contributing reasons remain unclear. OBJECTIVE: This multidisciplinary study aimed to examine health science students' opinions on the use of social media in health science education and identify factors that may discourage its use. METHODS: Data were collected from the Universitas 21 "Use of social media in health education" survey, distributed electronically among the health science staff and students from 8 universities in 7 countries. The 1640 student respondents were grouped as users or nonusers based on their reported frequency of social media use in their education. RESULTS: Of the 1640 respondents, 1343 (81.89%) use social media in their education. Only 462 of the 1320 (35.00%) respondents have received specific social media training, and of those who have not, the majority (64.9%, 608/936) would like the opportunity. Users and nonusers reported the same 3 factors as the top barriers to their use of social media: uncertainty on policies, concerns about professionalism, and lack of support from the department. Nonusers reported all the barriers more frequently and almost half of nonusers reported not knowing how to incorporate social media into their learning. Among users, more than one fifth (20.5%, 50/243) of students who use social media "almost always" reported sharing clinical images without explicit permission. CONCLUSIONS: Our global, interdisciplinary study demonstrates that a significant number of students across all health science disciplines self-reported sharing clinical images inappropriately, and thus request the need for policies and training specific to social media use in health science education.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.2196/mededu.6304

Cites

18 of 18 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • In an international survey among health science students from 8 universities in 7 countries, 20.5% across all health science disciplines self-reported sharing clinical images inappropriately.

    “An international survey among health science students, from 8 universities in 7 countries, registered that a significant number of students (20.5%) across all health science disciplines self-reported sharing clinical images inappropriately [32].”

    From Dangers and Benefits of Social Media on E-Professionalism of Health Care Professionals: Scoping Review (2021) · cites this paper as [32]

    The Abstract Methods describes a survey of students at 8 universities in 7 countries, and the Abstract Conclusions says students across health science disciplines reported inappropriate sharing. But the Abstract Results specifies that 20.5% (50/243) applies only to students who used social media “almost always,” not to all surveyed students across those disciplines. (LLM, conf=0.94)

    Experimental — accuracy not yet measured

  • Surveys that captured students' self-report of posted unprofessional behavior reported witnessing the investigated examples with varying frequencies.

    “Surveys that captured students’ self-report of posted unprofessional behavior (eg, evidence of being intoxicated, illegal drug use, posting patient information, sharing clinical images inappropriately, and depictions of an illegal act) reported witnessing the investigated examples with varying frequencies [32,42,43,55,59,62,64,73,114,118].”

    From Dangers and Benefits of Social Media on E-Professionalism of Health Care Professionals: Scoping Review (2021) · cites this paper as [32]

    The Results (Table 3; page unavailable) report students’ self-reported sharing of patient information and clinical images at varying frequencies. They do not report students witnessing those posts, or investigate the listed examples involving intoxication, drug use, or illegal acts. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • A total of 8 studies were conducted in several countries simultaneously.

    “A total of 8 studies were conducted in several countries simultaneously [32-39].”

    From Dangers and Benefits of Social Media on E-Professionalism of Health Care Professionals: Scoping Review (2021) · cites this paper as [32]

  • O'Sullivan et al have recognized the importance of schools using an evidence-based approach to policy creation and involving students in the process of creating these policies.

    “O’Sullivan et al [32] have also recognized the importance of schools using an evidence-based approach to policy creation and to involve students in the process of the creation of these policies.”

    From Dangers and Benefits of Social Media on E-Professionalism of Health Care Professionals: Scoping Review (2021) · cites this paper as [32]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Related

Authors
O'Sullivan, E., Cutts, E., Kavikondala, S., Salcedo, A., D'Souza, K., Hernandez-Torre, M., Anderson, C., Tiwari, A., Ho, K., & Last, J.
Affiliations
  • University College Dublin (IE)
  • University of Nottingham (GB)
  • University of Hong Kong (HK)
  • Tecnológico de Monterrey (MX)
  • University of British Columbia (CA)
Corresponding author
O'Sullivan, Elizabeth
ORCID
Venue
JMIR Medical Education
Publisher
JMIR Publications Inc.
Published
2017-01-04
Volume
3
Issue
1
Pages
e1
ISSN
2369-3762 (ISSN-L)
DOI
10.2196/mededu.6304
PMC
PMC5244031
OpenAlex
W2570962458
Cited by
28
References
18
Citations by year
2025: 3 · 2024: 1 · 2023: 3 · 2022: 1 · 2021: 4 · 2020: 6 · 2019: 5 · 2018: 3 · 2017: 2
FWCI
4.87
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • JMIR Medical Education (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Repository@Nottingham (University of Nottingham) (publishedVersion, cc-by)
  • Repository@Nottingham (University of Nottingham) (submittedVersion, cc-by)
  • Nottingham ePrints (University of Nottingham) (submittedVersion, cc-by)
Topics
  • Social Sciences › Health › Social Media in Health Education
  • Physical Sciences › Computer Science › Information Systems › Educational Technology in Learning
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Innovations in Medical Education
Keywords
social media, health science education, health sciences students, barriers to social media use, social media training, professional boundaries, institutional policy, student perceptions

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.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review