Bibliographic record
Abstract
Our medical colleagues are burning out at record rates.Ninety-one percent of US physicians have felt burned out at some point in their career. 1The pressures of quality improvement, electronic health records, 15-minute appointments, and waning connections with colleagues all contribute to the morass.Where do we turn to find relief and solace?Jillian Horton, a Canadian internist, reached the limits of her capacity as associate dean of students and clinician at the University of Toronto.The demands of teaching and caring for hospitalized patients became like a yoke.She found herself completely burdened by emails from students, paperwork for patient care, "and wishing everyone else would leave me the f*#k alone!"(p.13).At that point, she boarded a plane to Rochester, New York, to attend a retreat at the Chapin Mill Retreat Center for doctors suffering from burnout with founders and program directors Ron Epstein and Mick Krasner of the Mindful Practice in Medicine program at the University of Rochester.The book chronicles the journey of her rise and fall in academic medicine.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.027 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.019 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".