Care for Post Elective Coronary Artery Bypass Graft Surgery

Care for Post Elective Coronary Artery Bypass Graft Surgery Assessment for the care of patient with respiratory problem  following Coronary Artery Bypass Graft 3 vessels disease and Mitral Valve Repair Introduction This essay examines a case study of a male patient with a complex history who has undergone elective coronary artery bypass graft surgery, and suffered a number of recovery complications. Coronary artery disease is a common pathology in the Western population, perhaps due to lifestyle and dietary factors, including lack of exercise and smoking. The case history will be examined in the light of nursing care and current theoretical knowledge, looking at the individual needs of the patient and the potential interventions which could be employed to address this patient’s emergent and ongoing condition. Nursing care at any stage, critical, acute or chronic, must be an holistic process which takes into account all of the social, physiological, psycholological, emotional and spiritual needs of the person. Given the critical state of this individual’s health, however, some needs can be identified as more urgent that others. The case history will demonstrate the predominant needs of this patient as those connected with his respiratory function and status, and therefore while all aspects of the case will be considered, considerable attention will be paid to his respiratory needs, treatments and potential outcomes. The focus is on nursing care, which must address the emergent clinical picture whilst considering long term, mid term and short term outcomes in a client-centred context. Discussion The patient, who shall be called Mr S to protect confidentiality, was admitted on June 6th for an elective coronary artery bypass graft procedure, plus a Mitral Valve Repair. According to UHC (2007) a coronary artery bypass graft (CAB or CABG) is a surgical procedure in which a healthy blood vessel is transplanted from another part of the body into the heart to replace or bypass a diseased vessel. In this case, it is the treatment of choice for the patient whose history of myocardial infarction and coronary artery disease made him a prime candidate for corrective surgery. Coronary artery disease is defined as the failure of the coronary arteries to deliver oxygen and fuels for myocardial work (Emery and Pearson, 1998). Coronary artery disease is a leading cause of myocardial infarction (Emery and Pearson, 1998). According to STS (2007), mitral valve repair is an open heart procedure which aims to treat stenosis or regurgitation of the mitral valve, which is the inflow valve for the left side of the heart. In normal physiology, blood flows from the lungs, where it picks up oxygen, and into the heart through the mitral valve (STS, 2007). When it opens, the mitral valve allows blood to flow into the left ventricle, which then closes to keep blood from leaking back into the lungs when the ventricle contracts to pump blood into the systemic circulation (STS, 2007). In this case, mitral regurgitation has been diagnosed, which is probably consequential to Mr S’s ischaemic heart disease (Emery and Pearson, 1998). The patient history includes the following: post lateral MI treated with thrombolysis; shortness of breath on exertion; treatment with GTN; hypertensive disease; raised cholesterol; smoker (80-100 cigarettes a day, stopped smoking in 2000); umbilical hernia repair; removal of a benign growth on the thyroid gland; left ankle oedema; distal varicosities to the left extremity. Mr S is allergic to penicillin, overweight at 115kg and has been treated for the health consequences of his lifestyle for some time. Mr S underwent the procedure as planned, with the standard postoperative care. On return to the ward from theatre he was initially on synchronized intermittent mandatory ventilation, which is a system that was developed as a method of partial ventilatory support to facilitate liberation from mechanical ventilation (CCM, 2007). In this system, a demand valve is located within the system through which patients can take spontaneous breaths, without having to breathe through the ventilator apparatus, allowing the patient to breathe spontaneously while also receiving mandatory breaths (CCM, 2007). As the patient’s respiratory function improves, the number of mandatory breaths is decreased, until the patient is breathing unassisted on continuous positive airways pressure (CCM, 2007). Non invasive forms of ventilatory support have been found to be associated with improved patient outcomes (Peter et al, 2002), in a range of acute respiratory conditions including acute respiratory fail ure. Mr S was extubated after ten hours, placed on high flow oxugen via face mask at 50%, but PA02 was only 7 with quiet lung bases on auscultation, leading to the introduction of WCPAP, with a PEEP of 7.5. He was coughing but not expectorating, and developed a number of other postoperative complications which are listed below. His CVP was on 24mmhg and stable within that range. Blood Pressure went down to 80/50 mmHg, treated with gelofusion with no response. noradrenaline was started 07mic/kg/min Frusemide 20mg /hr with good effect; on the second day urine outputtailed off to 60-70ml/hr so the frusemide was increased in 40mg/hr with good effect. Urine output increased to a ratoe of 120-150mls/hr. Mr S has has 3 chest drains: mediastinal, pleural and pericardial . Mr S is ventricularly paced at around 90 beats, with an underlying bradycardia of 44 beats /min. Blood results: urea was 4.4 on the first day, 8.3 day two post-op; creatinene was initially 102, then 164, and on the third day it was 280. Noradrenaline was used, followed by some attempt to wean MR S of this level of support, but the MAP was not stable and could not be kept at 70, and so noradrenaline recommenced. Plans for discharge were postponed due to the WCPAP, the renal complications and the blood pressure issues. Mr S was had an Epidural with plain levopuvicaine at 5mls per hourincreased to 8 mls because of pain on movement; in addition to this he had a PCA (which was being used minimally), and regular Cocodamol. As can be seen, Mr S’s condition is quite serious with a range of complications from the surgery related to his postoperative recovery. Given than cardiac surgery has been performed and there are issues with maintaining blood pressure and cardiac rhythm, the two appear to be connected. Low cardiac output due to arrythmias are of some concern, and so all observations should be closely monitored. The area of concern for this essay, however, is the area of the respiratory complications, but brief mention will be made of the nursing considerations of the other aspects of his condition Nursing care focusing on his pain relief should include regular pain management, assessment of pain scores and sedation levels, and hourly pump checks on the epidural infusion and the PCA. These should be documented contemporaneously and comprehensively, and this information should be used for ongoing care planning, evaluation and communication with colleagues. Monitoring of intravenous infusions should include checking the IV site and cannula for patency or any signs of inflammation, checking that all the infusion lines are connected, and the pumps are set at the correct rate. Fluid balance should be recorded on the appropriate chart at the appropriate hourly intervals. The colour and consistency of the urine should also be noted. Fluid management is important in respiratory disease because excess fluid intake is prone to leak through the capillary membranes into the lung tissues (Peters, 1998). Vital observations should be recorded as specified by the medical and cardiothoracic teams. Pressure area care should be carried out, nutritional status should be monitored, and responses to medications noted. All medications should be administered as charted. Further to this, the chest drains must be observed, insertion sites assessed for signs of infection, and temperature monitored for signs of systemic responses to infection. The drain contents must be included in the fluid balance measurements, and must also be reported to the doctors, and observed for signs of haemorrhage. The drains must be kept off the floor but below the level of insertion of the tubing, to prevent the contents tracking back up towards the body, which would increase the risk of infection. For this reason, if Mr S is moved at any time or repositioned, or during procedures such as bedmaking and attending to hygiene needs, the tubing of all three drains should be clamped for the duration of the activity and then unclamped again afterwards. In addition to this, Mr S appears to need considerable respiratory support. The literature shows that satisfactory oxygenation can generally be achieved in most patients by the use of continuous positive end expiratory pressure (PEEP) using a continuous positive airway pressure (CPAP) mask with a PEEP valve of 5-10 cm of water. However, it has become apparent that Mr S cannot be safely weaned from this as yet. One option to consider would be NPPV, which is a treatment which has evolved from CPAP (Peters, 1998). It has been found to be very effective in providing ventilatory support for patients with respiratory disorders, particularly long term and in the home setting (Peters, 1998). This might be one option which could support Mr S in being discharged from the intensive care facility. Positioning and physical support to maintain this are also important (Peters, 1998). Therefore, Mr S should be nursed upright or semi-upright, well supported by pillows, but giving due consideration to pressure area care. Thorens et al (1995) suggest that the quality of nursing seems to be a measurable and importantfactor in the weaning from mechanical ventilation of patients with chronic obstructive pulmonary disease. While Mr S’s condition is not COPD, many aspects of his symptoms and, obviously, the environment in which he is being nursed, are similar to those described in this study by Thorens et al (1995). They suggest that below a threshold in the available workforce of ICU nurses, the weaning duration of patients from ventilation and other forms of mechanical ventilatory support increases dramatically (Thorens et al, 1995). Therefore, very close attention should be given to the education and number of ICU nurses (THorens et al, 1995), which in this instance could be vie wed from a managerial point of view, in ensuring that the appropriately skilled and experienced nurses are those allocated to the care of Mr S, and that his case should be seen as a priority. Addressing Mr S’s emotional and psychological needs are also important. While it is an extremely invasive and potentially life threatening procedure, coronary artery bypass graft surgery and mitral valve repair surgery are associated with positive patient outcomes(Moshkovitz et al, 1993). This was also an elective rather than an emergency procedure. This may mean that Mr S was not necessarily expecting such a problematic recovery period and so will need support adjusting to this. The same could be said for his family and carers, who would perhaps be somewhat shocked to find him still in a relatively serious condition. The support mechanisms available to him should be assessed, and it should be factored into the nursing care plan that time (often the most precious resource available to nurses) is allocated to him to ensure that he has amply opportunity to communicate with the staff. Communication difficulties may be associated with his condition, state of mind, level of conscio usness and the use of CPAP, and these must be taken into consideration. Wong et al (1999) discuss risk factors of delayed extubation and prolonged intensive care unit length of stay, which suggest that such occurrences are associated with higher levels of morbidity and longer periods of recovery. Whether this is due to the nature of the underlying condition, or the nature of the environment (or both), cannot be determined. However, it would appear that it is in Mr S’s best interests to be facilitated towards a level of wellness along the illness-wellness spectrum that is sufficient to warrant his discharge from the intensive care unit. It might be appropriate to consider different medication regimes, or to allow the physiological systems of his body more time to adjust to his postoperative recovering state. Another potential action might be to remove the epidural and encourage Mr S to use the PCA more appropriately, to support his pain control as a self-managed phenomenon, and to encourage a move towards increased independence, mobility and generally improved health. There is a degree of motor block evident from the epidural. Epidurals are also associated with low blood pressure, so this might be a factor in Mr S’s condition. The epidural would need to be removed under aseptic technique, and a small dressing placed over the site. The tip of the epidural catheter must be checked to make sure it is complete, and this noted in the patient’s records. Close monitoring of the patient’s blood pressure following this might allow the nurse to assess whether this has had a positive effect on Mr S’s blood pressure. Similarly, if Mr S is more mobile and able to move a little more independently, this might increase cardiac output and improve blood pressure. He is at considerable risk of post-operative thrombosis, in particular deep vein thrombosis and pulmonary embolism, and so mobilisation will be a key factor in his recovery and in preventing these complications. Hannan et al (2003) found that post-operative recovery from coronary artery bypass graft surgery can be adversely effected if the patient suffers from 6 or more comorbidities. Given his medical history, it is unsurprising that Mr S finds himself not recovering as quickly as potentially possible, and so it is important to maximise all opportunities to promote recovery and health. The use of low molecular weight heparin as a prophylaxis against deep venous thrombosis and pulmonary embolism is common in post-operative care, and is likely to be used here. However, there is a serious consequence of anti-coagulant therapy, which is the incre ased risk of haemorrhage, and so this again will need to be monitored for very carefully. Stanley et al (2002) suggest that neurocognitive decline is a continuing source of morbidity after cardiac surgery. This may be associated with cardiac arrythmias (Stanley et al, 2002). Mr S’s underlying bradycardia may then be a contributory factor in his long-term prognosis and this is why such intensive cardio-pulmonary support is warranted. Neurocognitive dysfunction is common after coronary artery bypass graft surgery (Stanley et a, 2002), and so assessments of this should form part of the ongoing care and monitoring of his condition. The pacemaker will also be monitored for functionality, and heart rhythm observed. Any changes will be assessed by the cardiothoracic team and any improvements towards normal rhythm noted. It is also important for nurses to consider multidisciplinary team input as a part of interprofessional working and client-centred care. For example, some research has demonstrated that a multidisciplinary approach to weaning from mechanical ventilation has been associated with greatly improved outcomes in the short and long term (Smyrnios et al, 2002). Mr S, given his condition, would be a prime candidate for pulmonary physiotherapy, which has been argued by some to be useful in the recovery process. Given the respiratory assessment findings, this may be used. However, the usefulness of respiratory physiotherapy for the prevention of pulmonary complications after cardiac surgery remains unproved(Pasquina et al, 2003). Therefore it would need to be a collaborative decision in conjunction with the consultant in charge of Mr S’s case. Conclusion This examination of Mr S’s case and history has demonstrated that he is suffering from a number of post operative complications associated with his surgical status, his past medical history and the range of comorbidities he is suffering. The diagnosis of his current condition must remain the area of responsibility of the doctors who are in chargeof his case. However, nursing interventions are a vital component of his care and potential for recovery. While doctors may diagnose and prescribe, it is the nursing staff who assess, monitor, administer therapies, and engage in the majority of prophylactic activities to support optimal return to wellness. This essay has also considered the need for an holistic approach to Mr S, viewing him as a person in the context of his own life rather than simply a set of conditions which much be treated and hopefully, resolved. However, the nature of his condition is serious, and until the cardiac and respiratory function issues are resolved, there is very little that can be done other than to support him and his body systems to continue to function, whilst engaging in nursing activities aimed at minimising further complications from his continued dependent and unwell state. There are a number of actions that can be taken, including pressure area care, fluid management, engagement with the multidisciplinary team, and pain management, all of which can contribute to supportive a positive prognosis for Mr S. However, he continues to require intensive nursing care until such time as he is able to be weaned off the CPAP and the noradrenaline which is helping to maintain the blood pressure. Until that time, all his needs will continue to be met by 24 hour intensive nursing care. References CCM (2007) http://www.ccmtutorials.com/rs/mv/page7.htm Emery, C. and Pearson, S. (1998) Managing coronary artery disease. In: Shuldham, C. (1998) Cardiorespiratory Nursing Cheltenham: Stanley Thornes. Hannan, E.L., Racz, M.J., Walford, G. et al (2003) Predictors of Readmission for Complications of Coronary Artery Bypass Graft Surgery JAMA. 290 773-780. Moschovitz, Y., Lusky, A. and Mohr, R. (1995) Coronary artery bypass without cardiopulmonary bypass: analysis of short-term and mid-term outcome in 220 patients. Thoracic and Cardiovascular Surgery 110:979-987. Pasquina, P., Tramer, M.R. and Walder, B. (2003) Prophylactic respiratory physiotherapy after cardiac surgery: systematic review British Medical Journal 327:1379 Peter, J.V., Moran, J.L., Phillips-Hughes, J. and Warn, D. (2002) Noninvasive ventilation in acute respiratory failure- A meta-analysis update. Critical Care Medicine. 30(3) 555-562. Peters: R. (1998) Respiratory failure: Adult Respiratory Distress Syndrome In: Shuldham, C. (1998) Cardiorespiratory Nursing Cheltenham: Stanley Thornes. Shuldham, C. (1998) Cardiorespiratory Nursing Cheltenham: Stanley Thornes. Smyrnios, N.A., Connolly, A., Wilson, M.M. et al (2002) Effects of a multifaceted, multidisciplinary, hospital-wide quality improvement program on weaning from mechanical ventilation. Critical Care Medicine. 30(6) 1224-1230. Stanley, T.O., Mackensen, G.B., Brocott, H.P. et al (2002) The Impact of Postoperative Atrial Fibrillation on Neurocognitive Outcome After Coronary Artery Bypass Graft Surgery. Anesthesia and Analgesia 94 290-295. STS (2007) http://www.sts.org/doc/410 Accessed 28-6-07 Thorens, J.B., Kaelin, R.M., Rainer, M. et al (1995) Influence of the quality of nursing on the duration of weaning from mechanical ventilation in patients with chronic obstructive pulmonary disease. Critical Care Medicine. 23(11) 1807-1815. UHC (2007) http://healthcare.utah.edu/healthinfo/adult/cardiac/glossary.htm Accessed 28-6-07 Wong, D.T., Davy, C., Kustra, R. et al (1999) Risk Factors of Delayed Extubation, Prolonged Length of Stay in the Intensive Care Unit, and Mortality in Patients Undergoing Coronary Artery Bypass Graft with Fast-track Cardiac Anesthesia: A New Cardiac Risk Score. Anesthesiology. 91(4) 936. Woods, S.L, Froelicher, E.S.S. and Motzer, S.U. (2000) Cardiac Nursing Philadelphia: Lippincott.

Psychological And Spiritual Guide Essay

Fear has diverse implications on a person. The presence of fear in a person is actually a manifestation on one’s behavior or influences. To collaborate in animal behavior, both fear and suspicion can turn one anxious and even make one do things beyond the normal state of a being. Cases such as sexual abuse or traumas are common amongst individuals in the contemporary society, although there are certain kinds of fear which have been inhibited by an individual since childhood and may have a hard time to cope up and overcome with it (Campbell, 2006). Fear creates limitations A person clouded with fear can be manifested in the actions of a person. As sociology suggests, fear can create impacts to the people around an individual; it could either gain sympathy or make another superior. The second effect then could be considered negative since it may be used by another to take advantage over the fearful person. Scholars even showed how fear overpowers one’s confidence in situations where one already relies to the concept of fear alone and feeling weary and helpless. As a matter of fact, studies show that fear sometimes takes over on the situation limiting the person of the capabilities that one utterly possesses. Aside from that fear also takes away the person from fulfilling one’s desire or one’s aim. Given the fact that the person has already been soaked in the river of fear—this also leads to low self-esteem—the person just goes along with what is happening and is afraid to make an appeal. Psychological implications of fear Fear is revealed when one feels the failure of control over main events and state of affairs in one’s life. In other words, this is a fear of loss of personal freedom. This is a predominant fear of people with substance addictions, battered wives and children, nursing home patients, and even the nations destitute. It also surfaces when an individuals’ indenture delayed sickness such as cancer or AIDS. Such fear is also established in people whose personality type is described as learned helpless-hopeless, people who think they have minute control of their lives. Behavioral changes caused by fear The concept of fear is coupled with fear of the unknown when one contemplates the subsistence of an afterlife and reaches no comfortable answers. Further, the conscious mind can’t comprehend life without itself, and the thought of nonexistence is less than comforting. Psychologists point out that many people, who exhibit fear is extremely vigilant, normally have behavioral changes or turn away from instances or materials or people which are in one point or another not supposed to be avoided. Moreover, few stress that anger and fear thoroughly two viewpoints which are indiscriminately diverse. There are others who believe that anger is really just another shadow of fear, inspired by that which generates a sense of awkwardness inside of us. Whether they are two completely different emotions, or derived from the same source but expressed differently, they are both very authentic. Like anger, fear is a factor of continued existence. In its most primal form, fear stimulates a substantial response to flee and hide from threats that are intimidating, overwhelming, and sometimes fatal (Lehrman & Harlow, 2006). Conclusions and further remarks Motivational properties are attributed to fear, and the fear-response may be thought of as occurring with such frequency as to equal emotional persistence. The distinction must be made however, for the fear-response which has been showed to be learnable and therefore directly dependent upon environmental cues rather than merely building up with successive noxious stimulations. Further, the responses of individuals to fear, either originating as a component or concomitant of pain, but learnable in the sense that it is capable of being elicited by some triggering factors and common practices that may be conceived as root of the fear. References Campbell, D. (2006). Inner Strength Defies the Skeptic: A Psychological And Spiritual Guide from Fear to Freedom. New York, NY: Immediex Publishing. Lehrman, N. S. , & Harlow, H. F. (2006). Emotionality and Fear. Science, 131(3415), 1700+1740.

Gender College Study

Sample details Pages: 14 Words: 4194 Downloads: 7 Date added: 2017/06/26 Category Statistics Essay Tags: Gender Essay Did you like this example? This chapter presents the results of the study. Included are an analysis of the five research questions and the six hypotheses of the study. This chapter concludes with a summary of the information presented in this chapter concerning the quantitative statistical findings of this study. As previously indicated, job satisfaction is a term that is difficult to describe as a single construct, and the definition of job satisfaction varies between studies (Morice Murray, 2003; Protheroe, Lewis Paik, 2002; and Singer, 1995). In higher education, a number of researchers have discussed the importance of continuous research on job satisfaction among community college faculty (Bright, 2002; Green, 2000; McBride, Munday, Tunnell, 1992; Milosheff, 1990; Hutton Jobe, 1985; and Benoit Smith 1980). A reason suggested for the continuous study of community college faculty, is the value of data received from such studies in developing and improving community college faculty and their practices (Truell, Price, Joyner, 1998). The purpose of this study was to examine job satisfaction of community college instructional faculty in regards to their role as teachers. Don’t waste time! Our writers will create an original "Gender College Study | Education Dissertations" essay for you Create order Analysis of Research Questions Research question one sort to describe the sociodemographic characteristics of community college instructional faculty. This research question included three variables (gender, age, and race/ethnicity). Sociodemographic Characteristics Gender There were 371 participants in the sample, of which 188 were male and 183 were female. In regards to gender, the analysis showed that 51% of the sample size included males and 49% of the sample size were female. Table 1 identifies the frequency and percentage results as they relate to gender of community college faculty. Table 1. Gender Distribution of Community College Instructional Faculty Gender Percent Frequency Male 51% 188 Female 49% 183 Total 100% 371 Age The sample size consisted of 371 participants. For age, the analysis displayed that 16% of the faculty were both under 30 and between ages 30 and 34 while17% were between ages 35 and 39. 15% of community college instructional faculty were between 40 and 44, while 14% were in the age range of 45 to 50. The last age range consisted of participants who were 50 or over, which was 21%. Even though the largest percentage of faculty members are 50 or over, faculty members who are 34 or under total 32% which indicates that the majority of faculty are under the age of 34. Table 2 identifies the frequency and percentage results as they relate to the variable of age of community college faculty. Table 2. Age Distribution of Community College Instructional Faculty Age Percent Frequency Under 30 16% 60 30-34 16% 60 35-39 17% 65 40-44 15% 57 45-49 14% 51 50 and over 21% 79 Total 100% 371 Race and Ethnicity The sample size consisted of 371 participants. The variable race/ethnicity showed that 83% of the participants were White, Non-Hispanic; 7% were Black, Non-Hispanics; 3% were Asian, Non-Hispanics; 1% were both American Indian, Non-Hispanics and Pacific Islanders Non-Hispanics; 2% were More than one race, Non-Hispanic; and 5% were Hispanics. Over 80% of the participants (308) were White, Non-Hispanic. Table 3 identifies the frequencies and percentages for the variable of race/ethnicity. Table 3. Race/Ethnicity of Community College Instructional Faculty Race/Ethnicity Percent Frequency White, Non-Hispanic 83% 308 Black, Non-Hispanic 7% 25 Asian, Non-Hispanic 3% 11 American Indian, Non-Hispanic 1% 1 Pacific Islanders, Non-Hispanic 1% 1 More than one race, Non-Hispanic 2% 7 Hispanics 5% 18 Total 100% 371 Research question two sort to describe the nature of employment characteristics of community college instructional faculty. This research question included three variables (rank, employment status, and tenure status). Nature of Employment Characteristics Employment Status There were 371 participants in the sample, of which 126 were employed full time and 245 were employed part time. In regards to employment status, the analysis showed that 34% of the sample size was employed full time and 66% of the sample size were employed part time. Table 4 identifies the frequency and percentage results as it relates to employment status of community college faculty. Table 4. Employment Status Distribution of Community College Instructional Faculty Employment Status Percent Frequency Full time 34% 126 Part time 66% 245 Total 100% 371 Rank The sample size consisted of 371 participants. In regards to rank, the analysis displayed that 9% of the sample size was identified as professors. Associate professors were identified at 5% of the sample size while Assistant professors were identified at 4%. Instructors were identified as 45% of the participants and lecturers were identified at 2%. Faculty with other titles were identified at 30% and 5% of the participants answered the question as not applicable. More than 40% of the participants (167) were identified as instructors. Table 5 identifies the frequency and percentage results as they relate to the ranking of community college faculty. Table 5. Rank Distribution of Community College Instructional Faculty Rank Percent Frequency Professor 9% 30 Associate professor 5% 19 Assistant professor 4% 15 Instructor 45% 167 Lecturer 2% 7 Other titles 30% 111 Not applicable 5% 22 Total 100% 371 Tenure Status The sample size consisted of 371 participants. In regards to tenure status, the analysis showed that 18% of the faculty were tenured; 6% of faculty were on a tenure track, but are not tenured; and 76% of faculty are not on a tenure track. More than 70% of the participants (282) were identified as faculty not on a tenure track. Table 6 identifies the frequency and percentage results as they relate to the tenure status of community college faculty. Table 6. Tenure Status of Community College Instructional Faculty Tenure Status Percent Frequency Tenured 18% 67 On tenure track, but not tenured 6% 22 Not on tenure track 76% 282 Total 100% 371 Job Satisfaction of Community College Instructional Faculty Research question three was designed to describe the job satisfaction of community college instructional faculty based on the eight components (Authority to make decisions; Benefits; Equipment/facilities; Instructional support; Overall; Salary; Technology-based activities; and Workload) of job satisfaction from the National Study of Postsecondary Faculty Survey NSOPF: 04. The sample size consisted of 366 participants. In regards to job satisfaction, the analysis showed that 73% of the faculty were very satisfied with authority to make decision; 34% of faculty were somewhat satisfied with benefits; 44% of faculty were very satisfied with equipment and facilities; 40% were somewhat satisfied with instructional support; 55% were very satisfied with overall job satisfaction; 42% were somewhat satisfied with salary; 53% were very satisfied with technology-based activities; and 50% of faculty were very satisfied with workload. Table 6 identifies the frequency and percentage results as they relate to the job satisfaction of community college faculty. Table 7. Job Satisfaction of Community College Instructional Faculty Satisfaction Percent Frequency Authority to Make Decisions Very satisfied 73% 268 Somewhat satisfied 22% 81 Somewhat dissatisfied 4% 14 Very dissatisfied 1% 4 Total 100 366 Benefits Very satisfied 27% 106 Somewhat satisfied 34% 127 Somewhat dissatisfied 19% 70 Very dissatisfied 18% 67 Total 100 371 Equipment/facilities Very satisfied 44% 161 Somewhat satisfied 38% 140 Somewhat dissatisfied 14% 51 Very dissatisfied 4% 15 Total 100 366 Instructional support Very satisfied 37% 134 Somewhat satisfied 40% 147 Somewhat dissatisfied 17% 62 Very dissatisfied 6% 23 Total 100 366 Job overall Very satisfied 55% 203 Somewhat satisfied 38% 141 Somewhat dissatisfied 6% 22 Very dissatisfied 1% 5 Total 100 371 Salary Very satisfied 29% 106 Somewhat satisfied 42% 157 Somewhat dissatisfied 18% 67 Very dissatisfied 11% 41 Total 100 371 Technology-based activities Very satisfied 53% 195 Somewhat satisfied 35% 129 Somewhat dissatisfied 9% 32 Very dissatisfied 3% 10 Total 100 366 Workload Very satisfied 50% 187 Somewhat satisfied 34% 127 Somewhat dissatisfied 11% 41 Very dissatisfied 4% 17 Total 100 371 Predictive Relationship between Sociodemographic Characteristics, Nature of Employment Characteristics and Job Satisfaction Research questions four and five examined the predictive relationship between gender, nature of employment, (rank, employment status, and tenure status) and job satisfaction of community college instructional faculty. Associated with this research question were six hypotheses. The hypotheses were tested using a multiple linear regression model that included two independent variables (gender and rank, gender and employment status, and gender and tenure status) and the eight components of the dependent variable, job satisfaction (Authority to make decisions regarding instructional practice, Benefits, Equipment/facilities for instructional use, Instructional support, Overall satisfaction, Salary, Technology-based activities, and Workload). The findings for each of the hypotheses are discussed below. Gender, Rank, and Job Satisfaction H01:There is no statistical difference in job satisfaction of community college instructional faculty based upon gender and rank. Ha1:There is a statistical difference in job satisfaction of community college instructional faculty based upon gender and rank. The regression model was not significant between the independent variables (gender and rank) and the dependent variable job satisfaction (Authority to make decisions regarding instructional practice), F (2, 363), = 0.280, p = .756 (See Table 8). A non-significant relationship was found between gender, rank, and component one. The coefficients were: t = -.321 (gender) and -.670 (rank), df = 363, and p .05 for both gender (.748) and rank (.504). Therefore, the null hypothesis was rejected because p .05 with alpha= .05. Table 8. Summary Regression Results for Authority to Make Decisions Model Sum of Squares df Mean Square F p Regression .234 2 .117 .280 .756 Residual 151.878 363 .418 Corrected Total 152.112 365 The regression model was not significant between the independent variables (gender and rank) and the dependent variable job satisfaction (Benefits), F (2, 363), = 4.203, p = .016. The total model produced an r-square value of 0.023 (See Table 9). The r-square value indicated that approximately 1% of the variation in benefits was accounted for by the combined variation of the 2 independent variables (gender and rank). The coefficients were: t = .050 (gender) and 2.897 (rank), df = 363, and p .05 for gender (.960) and p.05 for rank (.004). Therefore, the null hypothesis was rejected because p .05 and p .05 with alpha= .05. Table 9. Summary Regression Results for Benefits Model Sum of Squares df Mean Square F p Regression 9.431 2 4.716 4.203 .016 Residual 407.247 363 1.122 Corrected Total 416.678 365 R-Square = 0.023 The regression model was not significant between the independent variables (gender and rank) and the dependent variable job satisfaction (Equipment/facilities for instructional use), F (2, 363), = 1.045, p = .353. The total model produced an r-square value of 0.006 (See Table 10). The r-square value indicated that approximately 1% of the variation in equipment/facilities for instructional use was accounted for by the combined variation of the 2 independent variables (gender and rank). The coefficients were: t = .793 (gender) and -1.225 (rank), df = 363, and p .05 for both gender (.428) and rank (.221). Therefore, the null hypothesis was rejected because p .05 with alpha= .05. The regression model was not significant between the independent variables (gender and rank) and the dependent variable job satisfaction (Instructional support), F (2, 363), = .370, p = .691. The total model produced an r-square value of 0.002 (See Table 11). Table 10. Summary Regression Results for Equipment/facilities for Instructional Use Model Sum of Squares df Mean Square F p Regression 1.441 2 .721 1.045 .353 Residual 250.187 363 .689 Corrected Total 251.628 365 R-Square = 0.006 The r-square value indicated that approximately 1% of the variation in instructional support was accounted for by the combined variation of the 2 independent variables (gender and rank). The coefficients were: t = .392 (gender) and -.773 (rank), df = 363, and p .05 for both gender (.695) and rank (.440). Therefore, the null hypothesis was rejected because p .05 with alpha= .05. Table 11. Summary Regression Results for Instructional Support Model Sum of Squares df Mean Square F p Regression .570 2 .285 .370 .691 Residual 279.804 363 .771 Corrected Total 280.374 365 R-Square = 0.002 The regression model was not significant between the independent variables (gender and rank) and the dependent variable job satisfaction (Overall satisfaction), F (2, 363), = 13.505, p = .000. The total model produced an r-square value of 0.069 (See Table 12). The r-square value indicated that approximately 1% of the variation in overall satisfaction was accounted for by the combined variation of the 2 independent variables (gender and rank). The coefficients were: t = -5.169 (gender) and -.436 (rank), df = 363, and p .05 for gender (.000) and p .05 for rank (.663). Therefore, the null hypothesis was rejected because p .05 and p .05 with alpha= .05. Table 12. Summary Regression Results for Overall Satisfaction Model Sum of Squares df Mean Square F p Regression 19.269 2 9.634 13.505 .000 Residual 258.950 363 .713 Corrected Total 278.219 365 R-Square = 0.069 The regression model was not significant between the independent variables (gender and rank) and the dependent variable job satisfaction (Salary), F (2, 363), = .050, p = .951. The total model produced an r-square value of 0.000 (See Table 13). The r-square value indicated that approximately 0% of the variation in salary was accounted for by the combined variation of the 2 independent variables (gender and rank). The coefficients were: t = .220 (gender) and -.230 (rank), df = 363, and p .05 for gender (.826) and for rank (.818). Therefore, the null hypothesis was rejected because p .05 with alpha= .05. The regression model was not significant between the independent variables (gender and rank) and the dependent variable job satisfaction (Technology-based activities), F (2, 363), = .050, p = .819. Table 13. Summary Regression Results for Salary Model Sum of Squares df Mean Square F p Regression .091 2 .045 .050 .951 Residual 331.857 363 .914 Corrected Total 331.948 365 R-Square = 0.000 The total model produced an r-square value of .001 (See Table 14). The r-square value indicated that approximately 0% of the variation in technology based activities was accounted for by the combined variation of the 2 independent variables (gender and rank). The coefficients were: t = .081 (gender) and -.628 (rank), df = 363, and p .05 for both gender (.936) and rank (.531). Therefore, the null hypothesis was rejected because p .05 with alpha= .05. Table 14. Summary Regression Results for Technology-based activities Model Sum of Squares df Mean Square F p Regression .245 2 .123 .199 .819 Residual 223.219 363 .615 Corrected Total 223.464 365 R-Square = 0.001 The regression model was not significant between the independent variables (gender and rank) and the dependent variable job satisfaction (Workload), F (2, 363), = .557, p = .573. The total model produced an r-square value of 0.003 (See Table 15). The r-square value indicated that approximately 0% of the variation in workload was accounted for by the combined variation of the 2 independent variables (gender and rank). The coefficients were: t = .312 (gender) and -1.015 (rank), df = 363, and p .05 for both gender (.756) and rank (.311). Therefore, the null hypothesis was rejected because p .05 with alpha= .05. Table 15. Summary Regression Results for Workload Model Sum of Squares df Mean Square F p Regression 1.218 2 .609 .557 .573 Residual 396.607 363 1.093 Corrected Total 397.825 365 R-Square = 0.003 Gender, Employment Status, and Job Satisfaction H02:There is no statistical difference in job satisfaction of community college instructional faculty based upon gender and employment status. Ha2:There is a statistical difference in job satisfaction of community college instructional faculty based upon gender and employment status. The regression model was not significant between the independent variables (gender and employment status) and the dependent variable job satisfaction (Authority to make decisions regarding instructional practice), F (2, 363), = .070, p = .932 (See Table 16). A non-significant relationship was found between gender, employment status, and component one. The coefficients were: t = -.355 (gender) and .120 (employment status), df = 363, and p .05 for both gender (.723) and employment status (.904). Therefore, the null hypothesis was rejected because p .05 with alpha= .05. Table 16. Summary Regression Results for Authority to Make Decisions Model Sum of Squares df Mean Square F p Regression .040 2 .020 .070 .932 Residual 104.091 363 .287 Corrected Total 104.131 365 The regression model was not significant between the independent variables (gender and employment status) and the dependent variable job satisfaction (Benefits), F (2, 363), = 26.952, p = .000. The total model produced an r-square value of 0.129 (See Table 17). The r-square value indicated that approximately 1% of the variation in benefits was accounted for by the combined variation of the 2 independent variables (gender and employment status). The coefficients were: t = -.140 (gender) and 7.340 (employment status), df = 363, and p .05 for gender (.889) and p.05 for employment status (.000). Therefore, the null hypothesis was rejected because p .05 and p .05 with alpha= .05. The regression model was not significant between the independent variables (gender and employment status) and the dependent variable job satisfaction (Equipment/facilities for instructional use), F (2, 363), = 2.754, p = .065 (See Table 18). Table 17. Summary Regression Results for Benefits Model Sum of Squares df Mean Square F P Regression 51.741 2 25.870 26.952 .000 Residual 348.437 363 .960 Corrected Total 400.178 365 R-Square = 0.129 The coefficients were: t = -.016 (gender) and -2.347 (employment status), df = 363, and p .05 for gender (.987) and p .05 for employment status (.019). Therefore, the null hypothesis was rejected because p .05 and p .05 with alpha= .05. Table 18. Summary Regression Results for Equipment/facilities for Instructional Use Model Sum of Squares df Mean Square F p Regression 3.331 2 1.665 2.754 .065 Residual 219.489 363 .605 Corrected Total 222.820 365 The regression model was not significant between the independent variables (gender and employment status) and the dependent variable job satisfaction (Instructional support), F (2, 363), = 1.844, p = .160 (See Table 19). The coefficients were: t = -.308 (gender) and -1.897 (employment status), df = 363, and p .05 for gender (.758) and p .05 for employment status (.059). Therefore, the null hypothesis was rejected because p .05 and p .05 with alpha= .05. Table 19. Summary Regression Results for Instructional Support Model Sum of Squares df Mean Square F p Regression 2.651 2 1.326 1.844 .160 Residual 260.977 363 .719 Corrected Total 263.628 365 The regression model was not significant between the independent variables (gender and employment status) and the dependent variable job satisfaction (Overall satisfaction), F (2, 363), = .637, p = .529. The total model produced an r-square value of 0.003 (See Table 20). The r-square value indicated that approximately 0% of the variation in overall satisfaction was accounted for by the combined variation of the 2 independent variables (gender and employment status). The coefficients were: t = -.652 (gender) and -.924 (employment status), df = 363, and p .05 for both gender (.515) and employment status (.356). Therefore, the null hypothesis was rejected because p .05 with alpha= .05. The regression model was not significant between the independent variables (gender and employment status) and the dependent variable job satisfaction (Salary), F (2, 363), = .058, p = .944 (See Table 21). The coefficients were: t = .260 (gender) and -.216 (employment status), df = 363, and p .05 for gender (.795) and for employment status (.829). Therefore, the null hypothesis was rejected because p .05 with alpha= .05. Table 20. Summary Regression Results for Overall Satisfaction Model Sum of Squares df Mean Square F p Regression .516 2 .258 .637 .529 Residual 146.916 363 .405 Corrected Total 147.432 365 R-Square = 0.003 Table 21. Summary Regression Results for Salary Model Sum of Squares df Mean Square F p Regression .100 2 .050 .058 .944 Residual 315.441 363 .869 Corrected Total 315.541 365 The regression model was not significant between the independent variables (gender and employment status) and the dependent variable job satisfaction (Technology-based activities), F (2, 363), = .529, p = .589 (See Table 22). The coefficients were: t = -.334 (gender) and -.975 (employment status), df = 363, and p .05 for both gender (.739) and employment status (.330). Therefore, the null hypothesis was rejected because p .05 with alpha= .05. The regression model was not significant between the independent variables (gender and employment status) and the dependent variable job satisfaction (Workload), F (2, 363), = 13.418, p = .000. Table 22. Summary Regression Results for Technology-based activities Model Sum of Squares df Mean Square F p Regression .523 2 .261 .529 .589 Residual 179.130 363 .493 Corrected Total 179.653 365 The total model produced an r-square value of 0.069 (See Table 23). The r-square value indicated that approximately 1% of the variation in workload was accounted for by the combined variation of the 2 independent variables (gender and employment status). The coefficients were: t = 1.351 (gender) and -4.995 (employment status), df = 363, and p .05 for gender (.178) and p .05 for employment status (.000). Therefore, the null hypothesis was rejected because p .05 with alpha= .05. Table 23. Summary Regression Results for Workload Model Sum of Squares df Mean Square F p Regression 17.895 2 8.947 13.418 .000 Residual 242.062 363 .667 Corrected Total 259.956 365 R-Square = 0.069 Gender, Tenure Status, and Job Satisfaction H03:There is no statistical difference in job satisfaction of community college instructional faculty based upon gender and tenure status. Ha3:There is a statistical difference in job satisfaction of community college instructional faculty based upon gender and tenure status. The regression model was not significant between the independent variables (gender and tenure status) and the dependent variable job satisfaction (Authority to make decisions regarding instructional practice), F (2, 363), = 0.120, p = .887 (See Table 24). A non-significant relationship was found between gender, tenure status, and component one. The coefficients were: t = -.442 (gender) and .222 (tenure status), df = 363, and p .05 for both gender (.659) and tenure status (.825). Therefore, the null hypothesis was rejected because p .05 with alpha= .05. Table 24. Summary Regression Results for Authority to Make Decisions Model Sum of Squares df Mean Square F p Regression .073 2 .037 .120 .887 Residual 110.465 363 .304 Corrected Total 110.538 365 The regression model was not significant between the independent variables (gender and tenure status) and the dependent variable job satisfaction (Benefits), F (2, 363), = 9.706, p = .000. The total model produced an r-square value of 0.051 (See Table 25). The r-square value indicated that approximately 1% of the variation in benefits was accounted for by the combined variation of the 2 independent variables (gender and tenure status). The coefficients were: t = .015 (gender) and 4.405 (tenure status), df = 363, and p .05 for gender (.988) and p.05 for tenure status (.000). Therefore, the null hypothesis was rejected because p .05 and p .05 with alpha= .05. Table 25. Summary Regression Results for Benefits Model Sum of Squares df Mean Square F p Regression 20.959 2 10.479 9.706 .000 Residual 391.916 363 1.080 Corrected Total 412.874 365 R-Square = 0.051 The regression model was not significant between the independent variables (gender and tenure status) and the dependent variable job satisfaction (Equipment/facilities for instructional use), F (2, 363), = 3.790, p = .024. The total model produced an r-square value of 0.020 (See Table 26). The r-square value indicated that approximately 1% of the variation in equipment/facilities for instructional use was accounted for by the combined variation of the 2 independent variables (gender and tenure status). The coefficients were: t = .247 (gender) and -2.746 (tenure status), df = 363, and p .05 for gender (.805) and p .05 tenure status (.006). Therefore, the null hypothesis was rejected because p .05 p.05 with alpha= .05. The regression model was not significant between the independent variables (gender and tenure status) and the dependent variable job satisfaction (Instructional support), F (2, 363), = 2.705, p = .068. Table 26. Summary Regression Results for Equipment/facilities for Instructional Use Model Sum of Squares df Mean Square F p Regression 4.463 2 2.232 3.790 .024 Residual 213.758 363 .589 Corrected Total 218.221 365 R-Square = 0.020 The total model produced an r-square value of 0.015 (See Table 27). The r-square value indicated that approximately 1% of the variation in instructional support was accounted for by the combined variation of the 2 independent variables (gender and tenure status). The coefficients were: t = -.201 (gender) and -2.313 (tenure status), df = 363, and p .05 for both gender (.841) and p .05 tenure status (.021). Therefore, the null hypothesis was rejected because p .05 and p .05 with alpha= .05. Table 27. Summary Regression Results for Instructional Support Model Sum of Squares df Mean Square F p Regression 3.868 2 1.934 2.705 .068 Residual 259.599 363 .715 Corrected Total 263.467 365 R-Square = 0.015 The regression model was not significant between the independent variables (gender and tenure status) and the dependent variable job satisfaction (Overall satisfaction), F (2, 363), = .511, p = .600. The total model produced an r-square value of 0.003 (See Table 28). The r-square value indicated that approximately 0% of the variation in overall satisfaction was accounted for by the combined variation of the 2 independent variables (gender and tenure status). The coefficients were: t = -.484 (gender) and -.878 (tenure status), df = 363, and p .05 for both gender (.629) and for tenure status (.381). Therefore, the null hypothesis was rejected because p .05 with alpha= .05. Table 28. Summary Regression Results for Overall Satisfaction Model Sum of Squares df Mean Square F p Regression .391 2 .196 .511 .600 Residual 139.084 363 .383 Corrected Total 139.475 365 R-Square = 0.003 The regression model was not significant between the independent variables (gender and tenure status) and the dependent variable job satisfaction (Salary), F (2, 363), = .164, p = .849. The total model produced an r-square value of 0.001 (See Table 29). The r-square value indicated that approximately 0% of the variation in salary was accounted for by the combined variation of the 2 independent variables (gender and tenure status). The coefficients were: t = -.485 (gender) and -.296 (tenure status), df = 363, and p .05 for gender (.628) and for tenure status (.767). Therefore, the null hypothesis was rejected because p .05 with alpha= .05. Table 29. Summary Regression Results for Salary Model Sum of Squares df Mean Square F p Regression .269 2 .135 .164 .849 Residual 297.286 363 .819 Corrected Total 297.555 365 R-Square = 0.001 The regression model was not significant between the independent variables (gender and tenure status) and the dependent variable job satisfaction (Technology-based activities), F (2, 363), = 13.722, p = .000. The total model produced an r-square value of .070 (See Table 30). The r-square value indicated that approximately 1% of the variation in technology based activities was accounted for by the combined variation of the 2 independent variables (gender and tenure status). The coefficients were: t = 2.061 (gender) and -4.855 (tenure status), df = 363, and p .05 for both gender (.040) and tenure status (.000). Therefore, the null hypothesis was rejected because p .05 with alpha= .05. The regression model was not significant between the independent variables (gender and tenure status) and the dependent variable job satisfaction (Workload), F (2, 363), = 6.544, p = .002. The total model produced an r-square value of 0.035 (See Table 31). The r-square value indicated that approximately 1% of the variation in workload was accounted for by the combined variation of the 2 independent variables (gender and tenure status). The coefficients were: t = 1.140 (gender) and -3.455 (tenure status), df = 363, and p .05 for gender (.255) and p .05 for tenure status (.001). Therefore, the null hypothesis was rejected because p .05 and p .05 with alpha= .05. Table 30. Summary Regression Results for Technology-based activities Model Sum of Squares df Mean Square F p Regression 16.535 2 8.267 13.722 .000 Residual 218.700 363 .602 Corrected Total 235.235 365 R-Square = 0.070 Table 31. Summary Regression Results for Workload Model Sum of Squares df Mean Square F p Regression 8.363 2 4.182 6.544 .002 Residual 231.946 363 .639 Corrected Total 240.309 365 R-Square = 0.035 Summary The finding of this study showed that the gender of community college instructional faculty was almost equally distributed. In that, 51% were male and 49% were female. Apparently, community colleges are providing instructional opportunities not only for men, but also for women. The findings also showed that the majority of community college instructional faculty were below the age of thirty-four making a combined percentage of 32% for the age ranges of 34-30 and 30 and under, although 21% of community college instructional faculty are fifty years of age or over. Assuming a retirement age of 65, these data indicate the approximately 130 out 371 community college instructional faculty will have to be replaced in the next 15 years. This study also found that the community college instructional faculty ethnic make-up is White, Non-Hispanic at 83%. This indicates that the race of community college instructional faculty has a limited minority presence. Other findings from this study, such as employment status, showed that 66% of community college instructional faculty were employed in part-time status. This is consistent with findings in the literature regarding employment status. The findings also showed that 75% of community college instructional faculty were identified as instructors or had other titles. Since this study was examining the job satisfaction of community college instructional faculty regarding their role as teachers, the finding are not surprising that faculty viewed themselves as instructors. Finally, the finding for research question one, as it relates to tenure status showed that 76% of community college instructional faculty were not on a tenure track. The finding for research question three yielded that community college instructional faculty were either somewhat or very satisfied with all eight components (Authority to make decisions; Benefits; Equipment/facilities; Instructional support; Overall; Salary; Technology-based activities; and Workload) of job satisfaction ranging from 61% to 95%, with Benefits fairing the least at 61%. The results of the regression analysis conducted in this study showed that no significant relationship existed between gender and nature of employment (rank, employment status, and tenure status), and job satisfaction. All three hypotheses were tested at the .05 level of significance. The findings of this study revealed that none of the independent variables are predictive of job satisfaction of community college instructional faculty. The next chapter will present discussion, conclusions, implications, and recommendations of this study.

What Is Interprofessional Collaborative Care - 751 Words

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