How do we address each other in medicine?
Bibliographic record
Abstract
As we journey through our careers from medical school to residency to fellowship and then to faculty status, we are often confronted with the question of how to address our teachers, learners, and peers. Do we address them as Professor LastName or Doctor LastName, or should we address them by their first names? How would we like to be addressed ourselves, in various settings, and is it OK to speak up with our personal preferences? Although these may seem like simple questions, there are both operational and contextual factors that one should consider in choosing how to address each other in academic health care settings [1]. Often, as we move from institution to institution for training or new faculty positions, we enter a different cultural setting. We may not know the cultural expectations in the new setting, including how to address learners, peers, and those teaching us. Those around us may also not know how we would like to be addressed. Often the expected etiquette will differ from place to place, changing from one city or country to another, or from a more urban to a more rural location. Asking explicitly about this early on can be very helpful to someone just starting.
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 | 0.013 | 0.059 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.020 | 0.030 |
| Insufficient payload (model declined to judge) | 0.025 | 0.021 |
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".