Developing a Curriculum to Promote Professionalism for Medical Students Using Social Media: Pilot of a Workshop and Blog-Based Intervention
Bibliographic record
Abstract
BACKGROUND: As the use of social media (SM) tools becomes increasingly widespread, medical trainees need guidance on applying principles of professionalism to their online behavior. OBJECTIVE: To develop a curriculum to improve knowledge and skills regarding professionalism of SM use by medical students. METHODS: This project was conducted in 3 phases: (1) a needs assessment was performed via a survey of medical students regarding SM use, rationale for and frequency of use, and concerns; (2) a workshop-format curriculum was designed and piloted for preclinical students to gain foundational knowledge of online professionalism; and (3) a complementary longitudinal SM-based curriculum was designed and piloted for clinical students to promote both medical humanism and professionalism. RESULTS: A total of 72 medical students completed the survey (response rate 30%). Among the survey respondents, 71/72 (99%) reported visiting social networking sites, with 55/72 (76%) reporting daily visits. Privacy of personal information (62/72, 86%) and mixing of personal/professional identities (49/72, 68%) were the students' most commonly endorsed concerns regarding SM use. The workshop-format curriculum was evaluated qualitatively via participant feedback. Of the 120 students who participated in the workshop, 91 completed the post workshop evaluation (response rate 76%), with 56 positive comments and 54 suggestions for improvement. The workshop was experienced by students as enjoyable, thought provoking, informative, and relevant. Suggestions for improvement included adjustments to timing, format, and content of the workshop. The SM-based curriculum was evaluated by a small-scale pilot of 11 students, randomized to the intervention group (participation in faculty-moderated blog) or the control group. Outcomes were assessed quantitatively and qualitatively via personal growth scales, participant feedback, and analysis of blog themes. There was a trend toward improvement in total personal growth scores among those students in the blog group from 3.65 (0.47) to 4.11 (0.31) (mean [SD]) with no change observed for the students in the control group (3.89 [0.11] before and after evaluation). Themes relevant to humanism and professionalism were observed in the blog discussion. CONCLUSIONS: Most medical students surveyed reported using SM and identified privacy and personal-professional boundaries as areas of concern. The workshop format and SM-based curricula were well-received by students whose formative feedback will inform the refinement and further development of efforts to promote professionalism among medical students.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".