How we made professionalism relevant to twenty-first century residents
Bibliographic record
Abstract
The complexity of the current medical trainee work environment, including the impact of social media participation, is underappreciated. Despite rapid adoption of social media by residents and the introduction of social media guidelines targeted at medical professionals, there is a paucity of data evaluating practical methods to incorporate social media into professionalism teaching curricula. We developed a flipped classroom program, focusing on the application of professionalism principles to challenging real-life scenarios including social media-related issues. The pre-workshop evaluation showed that the participants had a good understanding of basic professionalism concepts. A post-workshop survey assessed residents' comfort level with professionalism concepts. The post-workshop survey revealed that the postgraduate trainees perceived significant improvement in their understanding of professionalism (p < 0.05). Resident responses also exposed some challenges of real-life clinical settings. There was an apparent contradiction between placing a high value on personal health and believing that physicians ought to be available to patients at any time. Participants' satisfaction with the course bodes well for continual modification of such courses. Innovative flipped classroom format in combination with simulation-based sessions allows easy incorporation of contemporary professionalism issues surrounding social media.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".