The Introduction of an Electronic Patient Care Information System and Health Care Providers’ Job Stress
Bibliographic record
Abstract
In this paper, the authors explore how the introduction of an electronic Patient Care Information System (PCIS) relates to changes in health care providers’ (HCPs’) perceptions of computer use, quality of patient care and job stress. Data were collected using a mixed-methods case study approach over a 20 month period following the introduction of this system. Initially stress levels appeared to increase, but over time declined significantly. After 3 months, the majority of HCPs reported they spent more time entering, retrieving and searching for patient information than before; however, these increases in computer use were unrelated to HCP stress. The potentially negative impact of the system on the quality of patient care was highly correlated with increased job stress. After 20 months, HCPs reported spending less time searching, entering and retrieving patient information, but these indicators of computer use were now highly correlated with stress. While some negative perceptions of the impact of the PCIS declined over time, HCPs reported ongoing stress related to concerns about quality of patient care even after 20 months of use.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".