Bibliographic record
Abstract
Trustworthiness disclosure: I've written a few of these words in a Vancouver hotel on a post-CUA week off, so clearly this won't be a "You must never work on your time off!" editorial ;) I t is correct to say that a well person doing good work requires time away from said work.This surely means attending to the day-to-day integration of work and home life, but today we're talking about vacation -an extended time to decompress, reboot, and detach from the workplace.A series of bracing highballs under a thatched umbrella.Traipsing through a cathedral you'd never heard of, that looks very much like the last one.Gliding through machine-groomed granular for four consecutive days.Idly puttering in your own home and town.Time off is restorative; no time off is corrosive.You know this and can find a body of evidence if you somehow need convincing.Start with recent work -authored by a who's who of MD wellness researchers -showing that 60% of U.S. physicians took 15 days of vacation per year (20% took 5 days!). 1 Among the small sample of urologists, 2/3 took 15 days vacation.More than three weeks of vacation was associated with decreased rates of burnout.Upcoming CUA census data is rosier -only 9% of respondents said they take fewer than three weeks vacation, and almost half take six or more weeks.This is hopeful data; take time off and reap the wellness benefits.Check.But is vacation time away from work, or just the workplace?The U.S. data also note that a third of MDs spend more than half an hour on workrelated tasks per day while on vacation (only 1/3 averaged zero minutes of work per day).It seems that the tendrils of the hospital are often tethers, and most of us will be attending to work while off.Your patient's C&S is pending, their K is dropping, hydro is worsening, flow is dwindlingand you're off on some adventure?Just a quick peek at the EMR, a wee email to your admin.You're going to have your phone with you anyway, right?Maybe your laptop for Netflix?The group chat will keep pinging, and ing, and LOLing.A glance
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".