Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
HEALTHCARE PROFESSIONAL BURNOUT has long been a well-known but not effectively addressed topic as the COVID-19 pandemic and the growing shortage of physicians have brought into the spotlight.In “Ohio Physicians’ Retrospective Pre-Post COVID-19 Pandemic Reports of Burnout and Well-Being” (page 8), Rebecca McCloskey et al discuss the results of a survey regarding physician burnout and mental health experiences prior to and during the COVID-19 pandemic.Achieving licensure and relocating to a new culture is difficult and stressful—akin to burnout. In “Facilitating the Path to Licensure and Practice: International Medical Graduates in Canada” (page 18), Ilona Bartman et al present a study that questions whether International Medical Graduates (IMGs) obtaining Canadian medical licenses in 2022 is more challenging or less challenging than it was in 2002. Efficiently licensing a large and diverse additional pool of healthcare professionals may reduce burden on the existing workforce.In “The Oregon Wellness Program (OWP): Serving Healthcare Professionals in Distress from Burnout and COVID-19” (page 27), Donald Girard and David Nardone expand upon the 2020 JMR OWP article by Divers et al. It includes an evaluative component of both client-users and mental health professionals, offering insight to understand factors, influence client stress, and guide programmatic optimizations.Medical licensing application questions regarding health conditions carry stigma. That may lead physicians to not disclose or seek care for health conditions—particularly mental health conditions. Fisayo Aruleba et al review this problem in “Do Medical Licensing Questions on Health Conditions Pose a Barrier to Physicians Seeking Treatment? A Literature Review” (page 35).And remember, as in the opening quote, we need you to make it.
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.
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 | 0.004 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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 it