Bibliographic record
Abstract
Introduction As medical students, we were always told that medicine is a tough road. The training is gruelling with endless hours of studying, patient care, and considerable sacrifices in our personal lives. Many came into this profession ready for long hours and difficult training. However, until residency, it is tough to envision what the experience really entails. Recently, a debate erupted in the Medical Twitter universe when Dr. Colleen Farrell, an Internal Medicine resident at Bellevue Hospital in New York, wrote a tweet decrying 27-hour resident call shifts as inhumane. Dr. Farrell argued that residents and staff physicians deserve protection against harsh working hours and conditions on par with workers in unionized professions. For instance, the Ontario Nurses Association closely regulates how long nurses can work in a given day with the minimal time nurses must receive for breaks1. The ensuing debate saw numerous residents and staff physicians joining the conversation on either side of the argument. Many physicians argued that extended call shifts are a necessary part of resident training which equips residents to work effectively and independently in future demanding roles. On the other hand, many suggest that the lack of adequate rest and humane working hours leaves residents ill-prepared to make decisions and may hinder patient care. In the end, Dr. Farrell received heavy backlash that led to her taking a break from Twitter. But what does the research say? Does reducing working hours improve resident wellness and patient safety? What is the impact on resident education? Can there be a way to balance resident wellness with competency and quality of care?
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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.030 | 0.008 |
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".