Moral distress in the pediatric intensive care unit: the impact on pediatric nurses
Bibliographic record
Abstract
PURPOSE: The purpose of this paper is to explore the concept of moral distress in particular as it impacts on the pediatric intensive care nurse caught between caring for infants and children who would not otherwise be alive were it not for the advances of modern medical technology, and their personal beliefs concerning the societal value of life at any cost. DESIGN/METHODOLOGY/APPROACH: Describes cases and real incidents to illustrate the moral distress experienced by-these nurses caught between caring for the children and at the same time interacting with the families. Such families are too often living on hope, with a profound faith in the ever advancing world of medical technology to keep loved ones alive with little thought to the consequences. FINDINGS: Suggests that the impact of moral distress on pediatric nurses, particularly as it relates to burnout, may well jeoparidize their ability to deliver effective care and is another unrecognized cost in the medical world. PRACTICAL IMPLICATIONS: Suggests that an ethical approach to care is necessary through hard to answer questions. Due to the fact that such questions are not often addressed, the author suggests consideration be given to medical ethicists to mediate and assist those caught in this dilemma. ORIGINALITY/VALUE: This paper will be of value to those concerned with how medical and life-saving technologies are outstripping our human abilities to comprehend and live with the consequences, and some of the ethical issues that arise.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".