Electronic Health Records and the Changing Roles of Health Care Professionals: A Social Informatics Perspective
Bibliographic record
Abstract
Our longitudinal study examines the changing roles of health care professionals (physicians, nurses, medical assistants, practice managers, and secretaries) before and after an EHR implementation in a large, multi-location group practice. We take a social informatics perspective and focus on the changing social identities of health care professionals as they adapt to their EHR-enabled roles. A year after go-live, a few professionals were still in reactive mode, trying to cope with the new system, but many others were actively shaping the technology and their roles in a variety of ways. A few went beyond shaping to find ways to provide additional value to themselves and to patients in ways that became possible only because of the EHR. In this paper, we explore these responses to the EHR as a basis for building theory about the potential for EHR systems to improve health care delivery, and the mechanisms by which that potential is realized.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".