Nursing Patients with Ventricular Assist Devices: An Interpretive Description
Bibliographic record
Abstract
CONTEXT-Although researchers have studied the experience of caring for patients with ventricular assist devices from the perspective of family caregivers, few reports address the experience of nursing patients with such devices. OBJECTIVE -To investigate the experience of nursing patients who have a ventricular assist device. DESIGN -A qualitative approach called interpretive description was used to conduct semistructured, 1-on-1 interviews. PARTICIPANTS-Six registered nurses with a range of clinical experiences were interviewed in a 1-year period from 2009 to 2010. Data were transcribed and analyzed by the researcher in conjunction with a research team. RESULTS-Four distinct themes were interpreted from the interview data: exclusive knowledge, human connection, ethics, and interdisciplinary stress and technology. CONCLUSION -Nursing patients who have a ventricular assist device is a complex experience. Nurses develop expert knowledge that is related to direct exposure to patients; this unique knowledge should be formally considered in team decision making. Nursing care of patients who have a ventricular assist device also has features that might result in overconnected nurse-patient relationships. Closely connected nurse-patient relationships intensified the emotional difficultly of experiences of exposure to illness or suffering, or exposure to an unpredictable dying trajectory. Nursing patients with ventricular assist devices can be difficult, and nursing leaders should be aware of the emotional reactions that can result from direct exposure to patients who might be perceived as very ill or suffering. Institutions with ventricular assist device programs should consider providing emotional support for health care workers who find this type of work emotionally difficult.
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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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".