Bibliographic record
Abstract
A residency program can be many things to the trainee: part workplace, part classroom, and part support network. Often, our residency programs become something of a second home. Given the stressful and demanding nature of residency education, we rely on the individuals around us to help create a safe and productive learning environment. The people around us often make the difference between a supportive environment and a toxic one.All residents can remember a call shift when everyone seemed to be having a bad night. One of those nights when everyone had only nasty things to say, when nobody was in the mood to help. It did not matter whether you were the attending, resident, nurse, clerical staff, or medical student—everyone else was the enemy. We all took part in making it an especially bad night.But why? Long hours, insufficient sleep, challenging cases, stress, and lack of staff support are frequently cited reasons. They are simply easy excuses to rationalize our behavior.Maybe it is a radical idea, but our nights (and days) might just go that much better if everyone was collegial. So what does collegiality look like? It starts with establishing a working relationship through a simple introduction to your team—this includes your direct service team, other health professionals, and consultants. It challenges us to recognize that everyone in the hospital is busy and that we're in it together for the benefit of our patients. It means genuinely listening to our colleagues, understanding their concerns, hearing their call for help, and moving beyond self-interest. It requires recognition and appreciation of the unique skill set and expertise that each of us brings to the patient experience.Communicating effectively and negotiating disagreement respectfully through an informed discussion, particularly in cases where generalist and specialist disagree on the reasons or urgency to do something, builds trust and credibility among resident colleagues. It involves making the extra little effort whenever possible.We will not stop advocating for improvements to residency training, including fatigue management strategies, better call room environments, more inclusive work environments, and valuing education over service. As we work toward making those values universal, however, we can do ourselves a favor and treat each other well.
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 | 0.019 | 0.263 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.006 |
| 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; both teacher heads agree on what is shown here.
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".