A Quantitative Study on Anonymity and Professionalism within an Online Free Open Access Medical Education Community
Bibliographic record
Abstract
The increasing use of social media to share knowledge in medical education has led to concerns about the professionalism of online medical learners and physicians. However, there is a lack of research on the behavior of professionals within open online discussions. In 2013, the Academic Life in Emergency Medicine website (ALiEM.com) launched a series of moderated online case discussions that provided an opportunity to explore the relationship between anonymity and professionalism. Comments from 12 case discussions conducted over a one-year period were analyzed using modified scales of anonymity and professionalism derived by Kilner and Hoadley. Descriptive statistics and Spearman calculations were conducted for the professionalism score, anonymity score, and level of participation. No correlation was found between professionalism and anonymity scores (rho = -0.004, p = 0.97). However, the number of comments (rho = 0.35, p < 0.01) and number of cases contributed to (rho = 0.26, p < 0.05) correlated positively with clear identification. Our results differed from previous literature, the majority of which found anonymity associated with unprofessionalism. We believe that this may be a result of the fostering of a professional environment through the use of a website with a positive reputation, the modelling of respectful behaviour by the moderators, the norms of the broader online community, and the pre-specified objectives for each discussion.
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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 | 0.018 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".