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The Mauk Model for Poststroke Recovery: Assessing the Phases
Despite the estimated 795,000 strokes occurring in America annually (American Heart Association, 2009), few practical models guide nurses when they provide quality care to stroke survivors. The Mauk Model for Poststroke Recovery is a theoretical framework concerning six phases of poststroke recovery. The purpose of this article is to discuss a pilot study detailing the ways in which nursing students used the Mauk model to identify these phases of stroke recovery via patient case examples. A sample of 30 volunteer nursing students read five case studies and determined the phase of stroke recovery. Descriptive statistics about sample characteristics and frequencies were calculated using SPSS 14 for Windows. Nearly 57% (n = 17) of the students rated all of the case studies to the correct phase. Ways in which the model might be clarified and used as a valuable tool when assessing the phase of stroke recovery are described.
Each year 795,000 Americans experience a new or recurrent stroke (American Heart Association [AHA], 2009). Stroke is the third-leading cause of death, and although the number of deaths related to stroke has dropped in the last decade, stroke remains a major cause of disability. Survivors of stroke may experience a host of physical, cognitive, and emotional deficits that often result in a change in perceived quality of life (Mumma, 1986). In addition to the physical and emotional strains of living poststroke, the financial burden is immense. The estimated direct and indirect cost of stroke in 2009 was $68.9 billion (AHA, 2009; Rosamond et al., 2007).
Rehabilitation nurses will continue to care for patients experiencing an acute stroke or subsequent chronic disability. The risks factors, medical treatments, and outcomes of physical and occupational therapy for poststroke survivors are documented in the medical literature. Patients’ and caregivers’ psychological responses have received increased attention in rehabilitation nursing research (King, Shade-Zeldow, Carlson, Feldman, & Philip, 2002; Steiner & Pierce, 2002). No single generally accepted practice model to guide nurses in assessing the phase of recovery of stroke survivors exists, however. The Mauk Model for Poststroke Recovery suggests six phases of recovery (agonizing, fantasizing, realizing, blending, framing, and owning) that healthcare professionals can use to target interventions to the unique needs of stroke survivors going through each phase of the rehabilitation process (Mauk, 2006). This article describes a pilot study that assessed the ability of nursing students to identify phases of stroke recovery using the Mauk Model and related factors that may have influenced this ability.
Review of the Literature
A number of models have been suggested as applicable in practice when caring for stroke survivors. Corbin and Strauss’s Chronic Illness Trajectory (Burton & Burton, 2000) and Complex Caring Trajectories (Allen, Griffiths, & Lyne, 2004) are two models that have been used. These models apply to many chronic illnesses but they may not adequately address the specificities of stroke survivors as they progress through rehabilitation and live with the effects of stroke. Although stroke can be a recurrent disorder, it does not follow a chronic illness pattern, making the trajectory models cited above less applicable to stroke than other disorders.
Other models have examined pieces of the stroke experience. Secrest and colleagues examined quality of life after stroke and developed the Continuity and Discontinuity of Self Scale (Secrest & Thomas, 1999; Secrest & Zeller, 2003, 2006). King and colleagues studied adaptation to stroke (King et al., 2002). However, few models attempt to provide an understanding of the entire stroke process from the survivor’s perspective. The Mauk model was developed using only the perspectives of survivors to obtain a clearer picture of recovery from an insider’s viewpoint.
In addition, few studies have been conducted regarding the ways in which educational programs improve nurses’ understanding of stroke, and these studies have not shown a direct link between nurses’ education and improved patient outcomes (Booth, 1999; Forster et al., 1999a, 1999b; Gibbon & Little, 1995; Jones, Carr, Newham, & Wilson, 1998). Davidson, Hillier, Waters, Walton, and Booth (2005) noted that no specific or unique nursing role has been identified or studied in stroke rehabilitation. They proposed that this is because nursing education does not include models to explain the nurse’s role in caring for patients with stroke. Burton and Burton (2000) and Thomas and colleagues (1999) identified the fact that conflicts arise among disciplines caring for stroke patients, in part because the nursing role is unclear. Although the Mauk model does not purport to clarify the nurse’s role in rehabilitation, the use of a poststroke rehabilitation model can be instrumental in guiding the interventions of nurses and other clinicians. Using the Mauk model, various members of the interdisciplinary team could assess the survivor’s place in the poststroke journey and better tailor unique interventions to the needs of each patient. Mauk previously proposed nursing interventions for each phase (2006), but this model could be used by other disciplines to do the same.
The Mauk Model of Poststroke Recovery (Mauk, 2006) provides a framework that is specific to stroke survivors and can help guide nursing practice. Stroke survivors were found to move through six phases of recovery (Table 1) in a somewhat orderly, yet individual, fashion if positive adaptations to stroke were made.
In Mauk’s research (Easton, 1999, 2001; Mauk, 2006), survivors initially reported feelings of fear, shock, and denial after their strokes (agonizing). The second phase, fantasizing, involves feeling that the effects of the stroke will go away, which are then found to be a “mirage of recovery.” Survivors were found to experience agonizing and fantasizing before the pivotal realizing phase, during which most survivors recognized there were lasting effects from the stroke. The amount of time spent agonizing and fantasizing appeared to be facilitated by age, life experience, and knowing the cause of the stroke (Figure 1). The realizing phase was influenced and perhaps buffered by expectations about having a stroke, social support, and faith (Easton, 2001).
During the last three phases, survivors make positive adjustments that help them adapt to a new life after stroke. Blending represents the ways in which survivors begin to learn and adapt, melding their past and present lives after stroke and adjusting to changes. Increased frustration is associated with this phase, but this also is the best time for nurses to teach because survivors are now ready to learn. Framing occurs when survivors actively seek the reason the stroke occurred. Owning, the final phase, involves acceptance of what has happened to them and a greater determination to have a good quality of life despite the effects of stroke.
Because the Mauk Model of Poststroke Recovery is relatively new, this study’s researchers wanted to determine whether nurses could easily apply it to their clinical practice. Nursing students generally are an informed group but have limited experience in patient care; consequently, the researchers chose them to form the sample for this study to evaluate the model’s ease of understanding and applicability. This pilot study assessed nursing students’ abilities to properly identify the phases of stroke recovery using clinical case scenarios.
Setting and Sample
A survey of junior, senior, and graduate nursing students was undertaken to determine students’ abilities to assess the phases of poststroke recovery using the Mauk model. A convenience sample of volunteers was obtained from nursing students attending the bachelor of science in nursing (BSN) or graduate program in a college of nursing at a moderately sized private Midwestern university. Subjects were recruited through flyers posted at the college of nursing, and the call for volunteers was made via e-mail to all nursing students meeting the criteria. A small honorarium was offered to participants. Approval for the study was obtained from the institutional review board at the university at which the study was conducted.
Three study sessions were set up to accommodate students’ schedules and obtain the desired number of subjects. Criteria for inclusion were age older than 18; junior, senior, or graduate nursing student; and a willingness to participate. Informed consent was obtained from each participant at each session before participation. Students were assured anonymity and confidentiality of responses. A sample size of 30 was desired and deemed (by the expert panel who established content validity of the case studies) sufficient for an initial pilot study with a sample of student nurses. Additional studies incorporating power analysis are planned with a larger sample size of clinicians.
Students received 15–20 minutes of explanation and instruction by the researcher about the Mauk Model for Poststroke Recovery (Figure 1), including a description of the six phases of the model, associated characteristics/subconcepts (Table 1), and ways to assess each phase. Another faculty member or a graduate nursing student also was present during the instruction session to provide assistance and ensure the researcher covered the same material during each session. Students then were given a packet of information that detailed the instructions for the study and instructions were verbally reviewed in front of the group by the researcher. The researcher then left the room while participants completed the study.
Participants reviewed five case studies developed by the researcher. Case studies were reviewed before finalization by three nurses with various levels of education and expertise to provide content validity and assess for clarity. These experts on content validity included one PhD-prepared researcher/faculty member who was certified in education, one master’s degree-prepared clinical nurse specialist in gerontological nursing, and one graduate nursing student majoring in adult health and in her last year of a master’s degree in nursing/family nurse practitioner program. The experts reached 100% agreement regarding the correct answers to the case study questions.
All study participants were provided with a copy of the model and a list of phases with associated characteristics/subconcepts for each phase. Each case study described a stroke survivor in a given phase. Students were asked to read the case study and identify which phase of the stroke recovery process they believed the survivor was going through. Participants were also asked to list as many factors in the scenario as they could that influenced their decision about the phase of recovery they chose for each case. Additional space was given for comments about their choices or further explanation about their decisions (Figure 2). Upon completion of the study, students placed the packet into an envelope. Students received a $10 honorarium for participation as they exited the room. Although there was no time limit set for students to complete the study, most participants were finished in 30–60 minutes.
To quantify the level of agreement between three or more raters giving categorical ratings, Fleiss’ generalized kappa statistic was used (Fleiss, 1971; King, 2004). This statistic is an extension of Cohen’s kappa statistic. No freely available algorithms for calculating interrater reliability among multiple raters exist on the Internet, and common statistical software programs have limited applications related to the generalized kappa statistic (King). In the data set for this study, the degrees of freedom were restricted because of the small number of cases being rated and one missing data point. Combined, these factors were barriers to conducting a Fleiss’ generalized kappa (J. E. King, personal communication, February 20, 2008). Instead, a percentage of agreement for each possible pair of raters was calculated, yielding 435 combinations. These percentages were averaged to yield a mean percentage of agreement. Although this approach does not make full use of the data (King) and fails to account for chance variation, calculating the mean percentage agreement was recommended given the circumstances (J. E. King, personal communication, February 20, 2008). SPSS 14.0 for Windows statistical package was used to compute descriptive statistics about sample characteristics and calculate frequencies.
The sample (N = 30) was a relatively homogeneous group of women enrolled in a Midwestern middle-sized private university (Table 2). The sample consisted of 7 juniors, 20 seniors, and 3 graduate students. Their ages ranged from 20–40 years (M = 22.93, SD = 5.16). One student was Asian and the remaining individuals were White. The majority of students were single (90%), although two were married and one was divorced. Two-thirds of the sample reported having cared for stroke patients; of these 20 students, 17 had cared for fewer than 10 patients and 3 had cared for between 10 and 20 patients. Only one student reported having used a theoretical framework when providing nursing care to stroke patients.
The ratings assigned by students are summarized in Table 3. The mean percentage agreement, an indicator of interrater agreement of 435 paired ratings, was 82.7%. Nearly 57% (n = 17) of the students rated all of the case studies to the correct phase. A correct phase was defined as the phase that the case study represented as indicated by the experts who had reviewed the case studies.
An analysis using chi-square was attempted to determine if there were any correlations between the demographic variables and correct rating of the phases. Because the sample was small and a high percentage of students correctly identified stages with scenarios, there were not a sufficient number of values in the cells for the chi-square statistics to be interpreted in a meaningful manner.
A higher percentage of respondents who had cared for stroke patients rated all the case studies correctly (66%) in comparison to those who had not cared for a stroke patient (40%). Furthermore, the percentage of correct ratings was always higher for nursing students who had cared for stroke patients in the past when compared to those who had not; the fantasizing phase, which all students correctly rated, was the exception. Data were further examined to compare students who had cared for a higher number of stroke patients to those caring for fewer stroke patients. Among the students who had cared for fewer than 10 stroke patients, 64.7% correctly rated all the case studies. This finding compared similarly with the 66.7% of students who had cared for 10–20 stroke patients and scored all the phases correctly. In addition, students who had cared for stroke patients always correctly identified the agonizing phase and never confused this phase with another phase.
The results of this small pilot study show that students were able to use the Mauk Model to assess phase of stroke recovery. Several items of interest arose from the data analysis that may further clarify the model. Overwhelmingly, participants choosing the incorrect phase of recovery selected the same wrong answer. This suggests there may be lack of clarity or appearance of overlap among some of the concepts. Of particular note, blending and owning seemed to be the most difficult to distinguish. The agonizing and fantasizing phases seemed clear to respondents in the study, as evidenced by only one incorrect response to assessment of both of those phases by all participants. The way the model appears on the page as having two distinct sections on either side of realizing may have helped participants to distinguish the early phases from later phases, although respondents appeared to be able to readily determine differences between these first two phases. There was some confusion in determining the realizing phase, particularly with regard to blending.
It should be noted that Mauk’s previous research reflected by the model suggested that stroke survivors who adapt well after stroke progress generally in a forward motion from the first phase to the last. However, as the model reflects, survivors may experience the first two phases (agonizing and fantasizing) in a rather circular pattern before moving to the pivotal phase of realizing, after which there is no more denying that the stroke occurred or fantasizing that it will go away. After survivors goes through the realizing phase, they also may experience parts of the last three phases in overlapping segments while still making progress toward owning. Survivors interviewed for Mauk’s earlier research (Easton, 1999, 2001) described revisiting earlier phases in the recovery process, but the time spent doing so was short-lived. After phases were accomplished, positive adaptation was considered to have been made. For example, one elderly female stroke survivor was observed to show signs of agonizing as she recounted her stroke, but she and the researcher both placed her in the owning phase because she had already accomplished the tasks for the other phases (Easton, 2001). So while there are ups and downs during the initial recovery process, after a survivor has reached realizing and worked to achieve the owning phase, there may be mild revisits or shades of the other phases but the owning phase is dominant. These underlying tenets of the model may have been confusing to study participants as they were given general instructions in a short time frame.
Most students were able to identify framing, but the two respondents who chose the incorrect response both chose blending, suggesting that the blending phase may need to be clarified. In looking at the model in light of student responses, the most difficulty in assessment was associated with distinguishing the last three phases. Interestingly, no student chose more than one phase of recovery nor circled more than one phase even though they were not told specifically that only one phase must be chosen.
The data also indicated that students who had previously cared for stroke patients were able to more correctly identify phases of recovery that those who had not. This was true regardless of the number of stroke patients for whom the students had cared. The agonizing phase also seemed to be clearer to those who had previously cared for stroke survivors. The authors acknowledge that the ways in which the case studies were written may have helped students to more easily identify one phase versus another.
This study’s sample may limit its generalizability. Subjects were recruited from a convenience sample of upper-level and graduate students, and most were seniors in a BSN program. There was little diversity in ethnicity, socioeconomic status, educational level, gender, or geographic location within the sample. Having only three experts review the case studies may be an additional limitation. Further study on this issue could be strengthened by increased diversity among participants, larger sample sizes, and the inclusion of clinicians with stroke expertise. A larger number of case studies for subjects to evaluate could also strengthen the study.
Validation of the usefulness of the Mauk model is essential if it is to be helpful in clinical practice. Nursing models such as this one can help nurses to implement evidence-based practice through correct assessment and identification of the phases of poststroke recovery. When rehabilitation nurses correctly assess the phase of stroke recovery, they can target interventions to meet the unique needs of the survivor at a particular point in recovery (Mauk, 2006). Data from this small pilot study also provided additional direction for refinement of the model, revealing areas that might be confusing to rehabilitation nurses. Further research needs to be completed to validate the model, especially research using experimental designs that may provide quantitative data related to patient outcomes.
This preliminary study suggests the Mauk Model of Poststroke Recovery is simple enough for students to use, at least in a case study situation, with minimal instruction. Students were largely able to successfully assess phases of stroke recovery. In general, the phases and associated characteristics are clearly articulated so students can distinguish between them. However, data suggest that some clarification regarding the last three phases of the model might help delineate their differences. The model may be useful in guiding care of stroke survivors, but further study and refinement is needed.
The authors would like to thank Dr. and Mrs. Robert Good for their kind contribution in support of this research. They would also like to thank Dr. Jason E. King, Baylor College of Medicine, for his consultation regarding data analysis.
About the Authors
Kristen L. Mauk, PhD DNP RN CRRN GCNS-BC GNP-BC FAAN, is a professor of nursing and Kreft Endowed Chair at Valparaiso University in Valparaiso, IN. Address correspondence to her at email@example.com.
Constance Lemley, MSN RN GCNS-BC, is an assistant professor at Valparaiso University in Valparaiso, IN.
Julie Pierce, MSN RN FNP, is a former graduate student of Valparaiso University in Valparaiso, IN.
Nola A. Schmidt, PhD RN CNE, is an associate professor at the Valparaiso University in Valparaiso, IN.
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