Evaluation of Online Health and Wellness Resources for Healthcare Professionals
Bibliographic record
Abstract
Two online resources, ePhysicianHealth.com and eWorkplaceHealth.com, were developed to help physicians and other healthcare professionals improve their health and well-being by providing them with relevant, up-to-date support and resources at no cost and with anonymous access. ePhysicianHealth.com is the worldâ??s first comprehensive, online physician health and wellness resource designed to help physicians and medical students be resilient in their professional and personal lives. eWorkplaceHealth.com is a new and original resource aimed to increase awareness and understanding of the issues and factors that may affect healthcare professionalsâ?? health at work. ePhysicianHealth.com includes 14 modules in French and English and eWorkplaceHealth.com comprises a four-module program in English. A program evaluation using quantitative and qualitative methods was conducted to obtain evidence regarding the usersâ?? perceptions of the learning resources and their impact on the specified outcomes. 
 Most participants felt they gained new knowledge from ePhysicianHealth.com and eWorkplaceHealth.com. They felt they had access to useful information through the resources, which increased their awareness of the various issues facing physicians today and where they can go to get assistance when in need.
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 | 0.009 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".