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Temporary is avoidance, forever is a lobotomy: Nurses' silence on unpopular patients
Title
Temporary is avoidance, forever is a lobotomy: Nurses' silence on unpopular patients.
Creator
Little, Daniel James., Florida Atlantic University, Coffman, Sherrilyn
Abstract/Description
This qualitative study of the phenomenon of nurse providing care to a client that the nurse does not like or determines to be unpopular was conducted with five professional nurses, who had experienced the phenomenon. Phenomenological method guided the inquiry through the narrative descriptions, from which essential descriptive themes of secrecy, avoidance, internalized conflict, specialness, and unfinishedness were uncovered and revealed by dwelling with the material. The implications for...
Show moreThis qualitative study of the phenomenon of nurse providing care to a client that the nurse does not like or determines to be unpopular was conducted with five professional nurses, who had experienced the phenomenon. Phenomenological method guided the inquiry through the narrative descriptions, from which essential descriptive themes of secrecy, avoidance, internalized conflict, specialness, and unfinishedness were uncovered and revealed by dwelling with the material. The implications for nursing education, nursing practice and nursing research are discussed.
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Date Issued
1996
PURL
http://purl.flvc.org/fcla/dt/15258
Subject Headings
Nurse and patient, Interpersonal relations, Hospital patients, Nurses--Attitudes
Format
Document (PDF)
THE RELATIONSHIP BETWEEN NURSE CARING AND READINESS TO TRANSITION FROM HOSPITAL TO HOME OR OTHER CARE SETTING
Title
THE RELATIONSHIP BETWEEN NURSE CARING AND READINESS TO TRANSITION FROM HOSPITAL TO HOME OR OTHER CARE SETTING.
Creator
Hernandez, Angelica C., Eggenberger, Terry, Florida Atlantic University, Christine E. Lynn College of Nursing, Christine E. Lynn College of Nursing
Abstract/Description
Quality nursing care has significant impact on patient outcomes. There are many factors that can affect quality nursing care like staffing shortages when the caring demands are high, which can affect patient care. Even though there are existing healthcare policies, evidence-based practices and incentives for healthcare settings who perform and meet the healthcare benchmarks, the United States healthcare performance is poor. The researcher aimed to conduct a study to understand the...
Show moreQuality nursing care has significant impact on patient outcomes. There are many factors that can affect quality nursing care like staffing shortages when the caring demands are high, which can affect patient care. Even though there are existing healthcare policies, evidence-based practices and incentives for healthcare settings who perform and meet the healthcare benchmarks, the United States healthcare performance is poor. The researcher aimed to conduct a study to understand the relationship between patients experience of nurse caring and patients’ readiness to transition from the hospital to home or other care settings. In addition, the predictors among the patients’ characteristics of patients’ readiness to transition from the hospital to home or other care settings were examined too. The research study was grounded in the Quality Caring Model (Duffy, 2018). Descriptive correlational research design was used in the study to examine the relationship between patients experience of nurse caring and patients’ readiness to transition from the hospital to home or other care settings. The study was conducted on one medical-surgical unit in an urban medical center in South Florida during a global pandemic. There were 103 participants who answered the demographic data survey, Caring Assessment Tool-V (CAT-V) and Readiness for Hospital Discharge Scale-Adult Form (RHDS-Adult Form). Descriptive and inferential statistics were conducted using SPSS version 28. Based on data analysis, there was a significant relationship between patients experience of nurse caring and patients’ readiness to transition from the hospital to home or other care settings (p=<.05). Therefore, patients with positive experiences of nurse caring will be more likely to transition from the hospital to home or other care settings. In addition, among the patient characteristics, the marital status could predict patients’ readiness (knowledge, coping ability and expected support subscales) to transition from the hospital to home or other care settings. Therefore, paying attention to the value of support systems of the patients will determine the readiness of the patients to go home or to be discharged to other care settings (p=<.05). The limitations of the study were low generalizability, inability to recruit 135 participants and selection bias (threat to internal validity).
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Date Issued
2022
PURL
http://purl.flvc.org/fau/fd/FA00013902
Subject Headings
Nursing-Patient relations, Nursing Care, Hospital to Home Transition
Format
Document (PDF)
lived experience of patients during family visits in the critical care setting
Title
The lived experience of patients during family visits in the critical care setting.
Creator
Remonte, Sonia D., Florida Atlantic University, Locsin, Rozzano
Abstract/Description
This study described the lived experience of patients during family visits in the critical care setting. Using Colaizzi's method of phenomenology interviews were conducted on six critical care patients in their homes two days after discharge from the hospital. From the transcribed interviews, three themes emerged: (a) Family visits enhance patients' well-being; (b) Family visits provide patient support systems; and, (c) Family visits facilitate communication among patients, the health care...
Show moreThis study described the lived experience of patients during family visits in the critical care setting. Using Colaizzi's method of phenomenology interviews were conducted on six critical care patients in their homes two days after discharge from the hospital. From the transcribed interviews, three themes emerged: (a) Family visits enhance patients' well-being; (b) Family visits provide patient support systems; and, (c) Family visits facilitate communication among patients, the health care team, and members of the family. Implications for nursing practice, nursing education, and research are presented.
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Date Issued
1997
PURL
http://purl.flvc.org/fcla/dt/15408
Subject Headings
Intensive care units--Patients, Hospital patients, Visiting the sick, Intensive care nursing
Format
Document (PDF)
Emergency department patients' perceptions of supportive nursing behaviors
Title
Emergency department patients' perceptions of supportive nursing behaviors.
Creator
Francis-Liburd, Julyn Clair., Florida Atlantic University, Coffman, Sherrilyn
Abstract/Description
Patients and nurses have been found to perceive support differently. This exploratory study was undertaken to: (a) identify those nursing behaviors perceived by emergency department patients as supportive, and (b) to identify the importance emergency room patients give to various nursing behaviors on a checklist. A sample of 30 emergency department patients completed a guided interview and the Supportive Nursing Behavior Checklist. The perceived attitude of the nurse toward the patient and...
Show morePatients and nurses have been found to perceive support differently. This exploratory study was undertaken to: (a) identify those nursing behaviors perceived by emergency department patients as supportive, and (b) to identify the importance emergency room patients give to various nursing behaviors on a checklist. A sample of 30 emergency department patients completed a guided interview and the Supportive Nursing Behavior Checklist. The perceived attitude of the nurse toward the patient and the availability of the nurse to the patient were the most important factors influencing patients' perceptions of support. The nurse being friendly and cheerful were the most important behaviors. The study has implications for nursing practice, nursing administration and nursing education. Suggestions are also given for further research.
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Date Issued
1994
PURL
http://purl.flvc.org/fcla/dt/15060
Subject Headings
Hospitals--Emergency services, Nurse and patient, Nurses--Attitudes, Nursing assessment, Caring
Format
Document (PDF)
HPCC based Platform for COPD Readmission Risk Analysis with implementation of Dimensionality reduction and balancing techniques
Title
HPCC based Platform for COPD Readmission Risk Analysis with implementation of Dimensionality reduction and balancing techniques.
Creator
Jain, Piyush, Agarwal, Ankur, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description
Hospital readmission rates are considered to be an important indicator of quality of care because they may be a consequence of actions of commission or omission made during the initial hospitalization of the patient, or as a consequence of poorly managed transition of the patient back into the community. The negative impact on patient quality of life and huge burden on healthcare system have made reducing hospital readmissions a central goal of healthcare delivery and payment reform efforts....
Show moreHospital readmission rates are considered to be an important indicator of quality of care because they may be a consequence of actions of commission or omission made during the initial hospitalization of the patient, or as a consequence of poorly managed transition of the patient back into the community. The negative impact on patient quality of life and huge burden on healthcare system have made reducing hospital readmissions a central goal of healthcare delivery and payment reform efforts. In this study, we will be proposing a framework on how the readmission analysis and other healthcare models could be deployed in real world and a Machine learning based solution which uses patients discharge summaries as a dataset to train and test the machine learning model created. Current systems does not take into consideration one of the very important aspect of solving readmission problem by taking Big data into consideration. This study also takes into consideration Big data aspect of solutions which can be deployed in the field for real world use. We have used HPCC compute platform which provides distributed parallel programming platform to create, run and manage applications which involves large amount of data. We have also proposed some feature engineering and data balancing techniques which have shown to greatly enhance the machine learning model performance. This was achieved by reducing the dimensionality in the data and fixing the imbalance in the dataset. The system presented in this study provides a real world machine learning based predictive modeling for reducing readmissions which could be templatized for other diseases.
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Date Issued
2020
PURL
http://purl.flvc.org/fau/fd/FA00013560
Subject Headings
Machine learning, Big data, Patient Readmission, Hospitals--Admission and discharge--Data processing, High performance computing
Format
Document (PDF)