Exploring digital professionalism
Bibliographic record
Abstract
The widespread use of digital media (both computing devices and the services they access) has blurred the boundaries between our personal and professional lives. Contemporary students are the last to remember a time before the widespread use of the Internet and they will be the first to practice in a largely e-health environment. This article explores concepts of digital professionalism and their place in contemporary medical education, and proposes a series of principles of digital professionalism to guide teaching, learning and practice in the healthcare professions. Despite the many risks and fears surrounding their use, digital media are not an intrinsic threat to medical professionalism. Professionals should maintain the capacity for deliberate, ethical, and accountable practice when using digital media. The authors describe a digital professionalism framework structured around concepts of proficiency, reputation, and responsibility. Digital professionalism can be integrated into medical education using strategies based on awareness, alignment, assessment, and accountability. These principles of digital professionalism provide a way for medical students and medical practitioners to embrace the positive aspects of digital media use while being mindful and deliberate in its use to avoid or minimize any negative consequences.
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 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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.052 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".