Information behavior in the context of improving patient safety
Bibliographic record
Abstract
Abstract Although it is assumed that information about patient safety and adverse events will be used for improvement and organizational learning, we know little about how this actually happens in patient care settings. This study examines how organizational and professional practices and beliefs related to patient safety influence (1) how health care providers and managers make sense of patient safety risks and adverse events, and (2) the flow and use of information for making improvements. The research is based on an ethnographic case study of a medical unit in a large tertiary care hospital in Canada. The study found that front‐line staff are task driven, coping with heavy workloads that limit their attention to and recognition of potential information needs and knowledge gaps. However, a surrogate in an information‐related role—an “information/change agent”—may intervene successfully with staff and engage in preventive maintenance and repair of routines. The article discusses four key functions of the information/change agent (i.e., boundary spanner, information seeker, knowledge translator, and change champion) in the context of situated practice and learning. All four functions are important for facilitating changes to practice, routines, and the work environment to improve patient safety.
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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 | 0.007 | 0.051 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".