Bibliographic record
Abstract
An estimate of the cost of retirement and reduction a 3 a in the healthcare professions including physicians. Con-ceptually, burnout is a syndrome consisting of three di-The focus on burnout could partly be attributed to the increasing awareness that physicians are exposed to Dewa et al. BMC Health Services Research 2014, 14:254 http://www.biomedcentral.com/1472-6963/14/254leave medicine [14] or change jobs [13,15]). It appearsM5T 1R8, Canada Full list of author information is available at the end of the articlemensions: emotional exhaustion, depersonalization and low personal accomplishment [1]. Estimates suggest that about one-third to one-half of physicians of various workplace factors putting them at risk of ongoing high work stress. Examples include long work hours [7] and work overload [8]. In turn, long-term exposure to high work stress can result in burnout [9]. Physician burnout is associated with low job satisfac-tion [10,11], decreased mental health [12] and decreased quality of patient care [6]. Recent evidence suggest a negative relationship between physician burnout and productivity (i.e., increased sick leave [13], intent to
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.457 | 0.215 |
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; the direct Gemma label and the distilled Codex classifier 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".