Technologically-Mediated Nursing Care: the Impact on Moral Agency
Bibliographic record
Abstract
Technology is pervasive and overwhelming in the intensive care setting. It has the power to inform and direct the nursing care of critically ill patients. Technology changes the moral and social dynamics within nurse-patient encounters. Nurses use technology as the main reference point to interpret and evaluate clinical patient outcomes. This shapes nurses' understanding and the kind of care provided. Technology inserts itself between patients and nurses, thus distancing nurses from patients. This situates nurses into positions of power, granting them epistemic authority, which constrains them as moral agents. Technology serves to categorize and marginalize patients' illness experience. In this article, moral agency is examined within the technologically-mediated context of the intensive care unit. Uncritical use of technology has a negative impact on patient care and nurses' view of patients, thus limiting moral agency. Through examination of technology as it frames cardiac patients, it is demonstrated how technology changes the way nurses understand and conceptualize moral agency. This article offers a new perspective on the ethical discussion of technology and its impact on nurses' moral agency. Employing reflective analysis using the technique of embodied reflection may help to ensure that patients remain at the centre of nurses' moral practice. Embodied reflection invites nurses critically to examine how technology has reshaped conceptualization, understanding, and the underlying motivation governing nurses' moral agency.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.030 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".