“I have the right to a private life”: Medical students’ views about professionalism in a digital world
Bibliographic record
Abstract
BACKGROUND: Social media site use is ubiquitous, particularly Facebook. Postings on social media can have an impact on the perceived professionalism of students and practitioners. AIMS: In this study, we explored the attitudes and understanding of undergraduate medical students towards professionalism, with a specific focus on online behaviour. METHODS: A volunteer sample of students (n = 236) responded to an online survey about understanding of professionalism and perceptions of professionalism in online environments. Respondents were encouraged to provide free text examples and to elaborate on their responses through free text comments. Descriptive analyzes and emergent themes analysis were carried out. RESULTS: Respondents were nearly unanimous on most questions of professionalism in the workplace, while 43% felt that students should act professionally at all times (including free time). Sixty-four free text comments revealed three themes: "free time is private time";" professionalism is unrealistic as a way of life"; and "professionalism should be a way of life". CONCLUSIONS: Our findings indicate a disconnect between what students report of what they understand of professionalism, and what students feel is appropriate and inappropriate in both online and real life behaviour. Curriculum needs to target understanding of professionalism in online and real environments and communicate realistic expectations for 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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".