Physicians’ electronic health records use at home, job satisfaction, job stress and burnout
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.
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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 | 0.001 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".