Nursing Identity and the Computer: The Impact of Care Realities on Information Systems Use
Bibliographic record
Abstract
Driven by developments in technology and communication, and by social, political and economic issues, the introduction of different information systems in nursing has risen significantly in recent years. However, little is known about the understanding of these systems by the nurses who are intended to use them.\nInformed by a Symbolic Interactionist approach, this research explored the experience of nurses interacting with information systems. Using grounded theory methods, the main sources of data were interviews, textual analysis and observation with nurses in three Canadian cities.\nThe key findings of this research are fourfold. First, the core category developed in this study is the care reality, a multi-faceted understanding of care that is central to the nursing identity, which adds a new level of understanding behaviour beyond the common attributes identified within nursing and information systems research. Second, this research identified the existence and importance of ready-to-hand and unready-to-hand information systems objects within nursing. Third, this research identified a care reality negotiation process, where each individual is continuously introduced to different care realities when they come into contact with co-workers or management who do not share the same care reality. The individual must then go through a negotiation process whereby each individual manages his or her care reality. The care reality negotiation process includes four phases: exposure, developing consciousness, sense-making and acclimatizing. Finally, the fourth result of this research was the identification of how an individual’s identity impacts on his or her understanding of information systems.\nThis research produces a theoretical understanding of the experiences of nurses interacting with information systems. It identified a link between an individual’s care reality and his or her behaviour toward all technology, including information systems.\nThe findings inform nursing research and practice, as well as contribute to the development, implementation and use of information systems in other areas of the modern healthcare system.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.013 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".