Physicians’ electronic health records use at home, job satisfaction, job stress and burnout
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
Objective: To determine how electronic health record (EHR) use at home impacts physician job satisfaction, job stress and burnout.Methods: This study looks at survey responses from 1,048 physicians in New York in 2016 to see how time spent on EHRs at home affected physician’s job satisfaction, job stress and burnout.Results: Accounting for demographic and practice values, physicians’ moderately high to excessive time spent on EHRs at home did not significantly affect job satisfaction but did significantly increase their odds of experiencing job stress by 50% and burnout by 46%. However, length and degree of documentation requirements and extension of work life into home by means of e-mail, completion of records and phone calls significantly correlated to decreased job satisfaction and increased job stress and likelihood of burnout.Conclusions: Although technology allows for physicians to work on electronic devices in various locations, healthcare administrators, policy makers and physicians alike should be aware of negative implications of excessive EHR use, documentation completion, e-mails and phone calls at home. Greater attention is needed on the human factors in the delivery of care and the importance of joy in the practice of medicine. Suggestions for organizational interventions are discussed.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it