Cited by 47Social Media in Health EducationInnovations in Medical EducationDiversity and Career in Medicine

Defining the Digital Self: A Qualitative Study to Explore the Digital Component of Professional Identity in the Health Professions

Open access
Ruan, Brandon, Yilmaz, Yusuf, Lu, Daniel, Lee, Mark, Chan, Teresa M·2020·Journal of Medical Internet Research

Published version of a preprint

Abstract

Background: Recent medical education literature pertaining to professional identity development fails to reflect the impact social media has on professional identity theory. Social media is transforming the field of medicine, as the web-based medium is now an avenue for professional development and socialization for medical students and residents. Research regarding identity development in social media has been primarily confined to electronic professionalism through best practice guidelines. However, this neglects other potential aspects pertinent to digital identity that have not yet been explored. Objective: This study aims to define the properties and development of the digital self and its interactions with the current professional identity development theory. Methods: A qualitative study was conducted using thematic analysis. A total of 17 participants who are social media education and knowledge translation experts were interviewed. The initial participants were from emergency medicine, and a snowball sampling method was used following their respective web-based semistructured interviews to enable global recruitment of other participants from interprofessional disciplines. The research team consisted of a diverse group of researchers including one current social media knowledge translation physician clinician educator, one postdoctoral researcher who is regularly engaged in social media knowledge translation, and 3 nonphysician research assistants who are not social media users. Half of the team conducted the initial coding and analysis, whereas the other 2 investigators audited the procedures followed. Results: A total of 4 themes were identified that pertain to digital identity. In the first theme, origins of initial digital identity formation were found to be derived from perceived needs in professional roles (eg, as a medical student or resident). The second theme consisted of the cultivation of digital identity, in which digital identity was developed parallel to professional identity. The third theme that emerged was the management between the professional and personal components of digital identity. Participants initially preferred keeping these components completely separate; however, attempts to do so were inadequate while the integration of both components provided benefits. The fourth theme was the management of real-life identity and digital identity. Participants preferred real-life identity to be wholly represented on the web. Instances of misalignment resulted in identity conflict, compromising one of the identities. Conclusions: Social media introduces new features to professional identity in the digital world. The formation of digital identity, its development, and reconciliation with other identities were features captured in our analysis. The virtual component of professional identity must not be neglected but instead further explored, as educational institutions continue to give more importance to navigating professional identity development.

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 · 10.2196/21416

Cites

47 of 47 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?

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

Research works printed but not linked: 2

  1. [31]Bebeau MJ Kenny N Shelton W Evidence-based character development Lost Virtue. Advances in Bioethics. Volume 10 2006 Melbourne, Australia Emerald Group Publishing Limited 47 86
  2. [49]Lupton D Digital Sociology 2015 New York, USA Routledge

Cited by

Sorted by citations

Authors
Ruan, B., Yilmaz, Y., Lu, D., Lee, M., & Chan, T. M.
Affiliations
  • McMaster University (CA)
  • University of British Columbia (CA)
Corresponding author
Chan, Teresa M
Venue
Journal of Medical Internet Research
Published
2020-09-29
Volume
22
Issue
9
Pages
e21416
ISSN
1438-8871 (ISSN-L)
OpenAlex
W3049290288
Cited by
47
References
47
Citations by year
2026: 6 · 2025: 5 · 2024: 9 · 2023: 8 · 2022: 7 · 2021: 12
FWCI
3.73
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Journal of Medical Internet Research (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Social Sciences › Health › Social Media in Health Education
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Innovations in Medical Education
  • Social Sciences › Gender Studies › Diversity and Career in Medicine
Keywords
digital identity, professional identity formation, social media, health professions education, medical students, residents (medical training), identity conflict, online professionalism, emergency medicine
MeSH terms
Female, Health Occupations, Humans, Male, Qualitative Research, Social Media

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