help needed in reviewing the attached document for grammar, APA

help needed in reviewing the attached document for grammar, APA professional writing. topic: Perceptions of Patients About Immunosuppressive Medication Adherence Specific Aims The purpose of this study is to understand underlying perceptions of patients about adherence to immunosuppressive medications. People experiencing immunosuppression, like those with genetic diseases, organ procedures, and some cancers, rely a lot on medication to suppress their immunity. However, while immunosuppressants are crucial for some therapies, their essential roles are often suboptimal, and they increase the risk of acquiring new illnesses, increasing the risk of hospitalization and poor health outcomes. In the era of increased patient literacy, patients on immunosuppressants are more likely to be aware of the adverse side effects and impact on immunity that these medications bring. Already, the literature suggests that patient perceptions substantially affect medical compliance (Bendersky et al., 2023; Posluszny et al., 2022). However, the understanding of how patients on immunosuppressants perceive their drugs and how that perception affects their adherence remains mixed and limited (Posluszny et al., 2022). Patients using these medications deal with complex doses and schedules and constant adjustments that make their usage quite challenging. These ever-changing therapies, coupled with trust in healthcare systems, affect how patients perceive their medications. Patients misunderstand or feel overburdened by these doses and the psychosocial and financial factors cause financial stress and mental challenges. Still, these factors are explored in other medical conditions but their examination in the immunosuppressed group remained an underexplored area. This lack of literature affects the prospects of developing tailored interventions for this unique patient group. Immunosuppressed patients are a unique group due to their extraordinary circumstances where they utilize life-saving drugs known to have a significant impact on their immunity against diseases. Understanding the unique barriers and facilitators of care will deliver first-hand information about what affects them, creating a foundation for effective measures to relieve their challenges (Aberumand, et al., 2020). Conducting an empirical study with this population will reveals what precise factors affect non-compliance, championing patient-centered care that has long been promoted as standard practice in nursing and healthcare systems. The study’s aim to understand underlying perceptions also promotes the patient-provider communication. Good relationships require empathy and compassion and examining patient perceptions provides a basis for understanding how patients think to better address their fears and concerns. Trusting relationships require collaboration and a feeling of being valued which can be achieved only when providers know the underlying thought patterns that patients have (Aberumand et al., 2020). Ultimately, understanding the unique viewpoints within this patient group will be the foundation for building a framework to address adverse patient perceptions on medications while enhancing medication adherence for optimal health outcomes and quality of life. The research will enhance the body of literature examining immunosuppressed groups, providing a foundation for more research to design tailored measures to balance medication adherence, patient experiences, and outcomes (Aberumand et al., 2020). Also, the study will provide significant insights into medication adherence, providing a basis for suggesting possible interventions. Interventions rely on the unique perceptions that patients have as these inform the pathways through which patients develop nonadherence. Patient perceptions are unique and exploring the factors affecting immunosuppressed patients will lead to targeted interventions that help alleviate the struggles that these patients encounter, improving their experiences and outcomes. Therefore, the present study will examine the study aims guided by the research question: What are the perceptions of patients about adherence to immunosuppressive medications? This research question will utilize qualitative interviews and surveys to deliver exhaustive insights into the unique factors affecting immunosuppressed patients. The study will utilize this design to explore the perceptions within the patient group, providing evidence of the precise underlying factors affecting this specific group. Literature Review Literature This section will examine the perceptions of patients about adherence to immunosuppressive medications. Instances of genetic diseases, organ procedures, and some cancers force patients to rely a lot on medication to suppress their immunity. However, adherence to these medications is relatively low. Adherence to immunosuppressive medications remains a critical factor in improving patient outcomes This review purposefully synthesizes existing research on patients’ perceptions regarding adherence to immunosuppressive medications. Through a thematic analysis, key emerging themes include patient beliefs and perceptions, demographic and psychosocial factors, and barriers and facilitators of adherence. Empirical evidence supports that patients’ beliefs significantly impact their adherence to medication. Kostalova et al. (2021) focused on patients’ beliefs about immunosuppressants and provided evidence that these beliefs significantly impact adherence. Their longitudinal observational study, conducted over three years, used validated questionnaires to assess belief changes. They found a decrease in the Necessity scale score and an increase in the Concerns scale score, resulting in a lower Necessity-Concerns Differential. They were able to conclude that there was a growing apprehension towards medication over time. This finding correlates with lower adherence. The methodology was robust in that it used repeated measures and validated questionnaires to assess dynamic beliefs and adherence behaviors. However, the small sample size and potential under-reporting of non-adherence may affect the generalizability of the results. Similarly, Zhang et al. (2021) integrated the health belief model and theory of planned behavior to understand medication non-adherence among renal transplant patients. Beliefs about medication, particularly perceived barriers and attitudes, became apparent as key players in adherence. Their findings were based on a cross-sectional study. The study used structural equation modeling to analyze data from many patients. Eventually, their approach offered a comprehensive framework for predicting non-adherence. The large sample size enhanced the generalizability of the findings, even though reliance on self-reported data may have introduced bias. Perceptions and Experiences Bendersky et al. (2023) provided qualitative insights into patients’ experiences with medication adherence. They used focus groups. Participants discussed their challenges and strategies related to adherence. From the discussion, patients understood the significance of medication adherence. However, significant support gaps existed. This study’s qualitative approach was suitable for capturing the nuanced experiences of participants. It adequately showcased the relationship between patient perceptions and adherence behaviors. However, the small sample size and single-center focus limit the generalizability of the results. Another qualitative study by Ozdemir Koken et al. (2023) explored liver transplant recipients’ experiences with medication adherence through in-depth personal interviews. The study identified factors such as forgetfulness, regimen complexity, and social challenges as significant barriers. Positive influences, including patient awareness and family support, were also identified. This phenomenological methodology gave a deep understanding of the subjective factors affecting adherence even though a small sample size and single-center focus limit the broader applicability of the findings. Demographic and Psychosocial Factors Sociodemographic Influences Sociodemographic factors have a role in influencing medication adherence and Ganjali et al. (2019) proved it. Through a cross-sectional investigation, they found that gender and quality of life contributed greatly to adherence with women, and those with a higher quality of life were likelier to stick with their prescribed regimen. Although the findings were robust due to a large sample size, the fact that data collection relied on self-reported data may have underestimated non-adherence. Analyzing the study by Posluszny et al. (2021) proved evidence of a relationship between the education level of patients and the type of disease they got and how they choose to adhere to medications. It was practical and easy for the study to conduct a detailed exploration of those influences. This is because the study used a prospective design involving validated measures and statistical analyses. However, the study focused predominantly on the spousal caregiver dyad. This element limits the generalizability of the results, and the lack of racial diversity affects the broader applicability of the findings. Psychosocial Dynamics Hamama-Raz et al. (2023) took a different approach. They found that adolescents’ illness cognition of helplessness and perceived family cohesion significantly predicted barriers to adherence. Their mixed-methods approach provided comprehensive findings that enhanced understanding of the complex interactions between family dynamics and health outcomes. However, the study relied on self-reported measures. It used a specific sample of kidney transplant recipients and their families, which may limit the generalizability of the results to other chronic illness populations. Barriers and Facilitators of Adherence Several barriers have been identified and Ganjali et al. (2019) identify some of them. This study elaborated that patients fail to comply with their prescriptions due to complex medication regimens and forgetfulness. Ozdemir Koken et al. (2023) also had similar sentiments. To be specific, Ozdemir Koken et al. (2023) noted that social and environmental challenges impede most patients from sticking to their regimens. The need to formulate interventions to address these obstacles is emphasized by both studies. The cross-sectional design of Ganjali et al.’s study limits long-term analysis. Still, on the other hand, Ozdemir Koken et al.’s qualitative approach provides rich and contextual insights generalizability is limited. Chen et al. (2022) identified similar barriers in their study of kidney transplant recipients. Forgetfulness and the complexity of regimens are prominent issues. Their internet-based survey revealed a high prevalence of non-adherence. As a solution, the study emphasized the importance of consistent physician follow-up. The potential sampling bias from internet-based recruitment and the small sample size limits the generalizability of their findings. Facilitators of Adherence Other studies have gone to the extent of identifying facilitators of adherence. As reported by Kostalova et al. (2021), a higher baseline necessity belief and patient awareness and family support, as noted by Ozdemir Koken et al. (2023), are some of the identified positive influences. The studies highlighted that these positive influencers could be leveraged when formulating targeted interventions to enhance adherence. Kostalova et al.’s longitudinal approach and use of validated questionnaires provide robust insights, while Ozdemir Koken et al.’s qualitative approach offers detailed, contextual understanding. Chen et al. (2022) found that having a fixed doctor for follow-up visits significantly improved adherence. The study suggested that stable patient-physician relationships facilitate better communication and trust. With better communication and trust, adherence is guaranteed. This finding aligns with the insights from Bendersky et al. (2023), who noted that support gaps significantly impeded adherence. This indicates that improving support systems could enhance adherence outcomes. Chen et al.’s logistic regression analysis was suitable for identifying associations, though the study’s limitations include potential sampling bias and a small sample size. Conclusion The studies under this review have collectively highlighted the multifaceted nature of adherence to immunosuppressive medications. These studies have identified patients’ beliefs and perceptions, influenced by demographic and psychosocial factors, as influencing adherence behaviors. Barriers and facilitators to adherence also exist. As for barriers, patients have reported regimen complexity, forgetfulness, and social challenges as reasons for non-adherence. A patient who is aware of their condition and medications, has adequate family support, and has consistent follow-ups with a care provider is likelier to adhere to their regimen. Methodological approaches varied, with quantitative and qualitative methods providing valuable insights, though each study had limitations affecting the generalizability of results. Future research should aim for larger, more diverse samples and consider longitudinal designs to understand long-term adherence trajectories better and develop effective interventions. Conceptual Framework The theoretical framework guiding this study is the health belief model (HBM). The health belief model (HBM) is a psychological model that attempts to explain and predict health behaviors by focusing on the attitudes and beliefs of individuals. The HBM suggests that a person’s belief in a personal threat of an illness or disease together with a person’s belief in the effectiveness of the recommended health behavior or action will predict the likelihood the person will adopt the behavior (LaMorte, 2022). This framework is appropriate for my study because it examines patients’ beliefs about medication adherence. This model consists of six components. They are perceived severity, perceived susceptibility, perceived benefits, perceived barriers, cues to action, and self-efficacy. Perceived susceptibility addresses an individual’s assessment of their risk of getting the condition. (LaMorte, 2022). For example, a person who believes they are at high risk of developing diabetes may be more likely to engage in behaviors to prevent it. Perceived severity is the individual’s belief about the of contracting an illness or of leaving it untreated. For instance, if someone believes that the consequences of having a heart attack are severe, they may be more motivated to take preventive measures. Perceived benefits are an individual’s assessment of the positive outcomes of adopting a health behavior. For example, if a person believes that exercising regularly will significantly reduce their risk of heart disease, they are more likely to engage in physical activity. Perceived Barriers is the believe that the benefits of taking the action outweigh the costs or barriers. This involves an individual’s evaluation of the obstacles to behavior change. For instance, if someone perceives that the cost of a gym membership is too high, they may be less likely to exercise, even if they believe it would benefit their health. Cue to Action is exposed to factors that prompt action. These cues can be internal (e.g., symptoms of a health condition) or external (e.g., advice from friends, media campaigns). For example, a person might decide to get a flu shot after seeing a public service announcement about the dangers of the flu. Self-Efficacy, the patients believe they can successfully perform the action. This component was added to the model later and refers to an individual’s confidence in their ability to act. For example, if someone believes they can successfully quit smoking, they are more likely to attempt to do so. In this study, the health belief model can be summarized as modifying factors, individual beliefs and actions. Modifying factors are age, gender, income, transplant history, and tobacco use. Individual beliefs are transplant failure, severity of transplant failure, and benefit of medication adherence. Action refers to medication adherence or medication non-adherence. Concept Map Image transcription text Modifying Individual Factors beliefs Action . Age . transplant . medication . Gender failure adherance . Education . severity of non- income transpla… Show more Research Methodology and Design Methodology This study will use a qualitative methodology to examine patients’ perceptions about immunosuppression medication adherence. The research design will be qualitative description. Qualitative descriptive research generates data that describe the ‘who, what, and where of events or experiences’ from a subjective perspective. This design is appropriate for this study because it recognizes the subjective nature of the problem, the different experiences patients have and will present the findings in a way that directly reflects or closely resembles the terminology used in the research question: What are the perceptions of patients about adherence to immunosuppressive medications? (Bradshaw et al., 2017). Sample Population The sample will be drawn from patients receiving care at the Mid-Hudson Valley transplant center. Sample Size Quota sampling determined a minimum sample size of 36 participants. This was determined using three initial income strata variables (low, middle, and high income) multiplied by 12 categories consisting of three gender categories (male, female, transgender), five educational categories (< H.S, HS or GED, some college, college degree and more than college), and four age categories (30-35, 36-40, 41-50, and 50+). Sampling Techniques This study will use non-probabilistic sampling techniques. Non-probabilistic sampling techniques are methods where not all members of the population have a chance of being selected non-probabilistic sampling techniques are purposeful, convenience, or snowball. The study will use purposeful sampling as a non-probabilistic technique (Boyd et al., 2023). This technique aims to include individuals with specific characteristics or experiences relevant to the study's aims. Purposeful sampling will be used to recruit the participants. Purposeful sampling is a non-probability sampling technique commonly used in qualitative nursing research. This method involves selecting participants based on specific characteristics or qualities that align with the research objectives. The goal is to gain in-depth understanding and insights from a targeted group rather than to generalize findings to a larger population. (Palinkas et al., 2015) Sample Inclusion Characteristics The inclusion characteristics of the participants are Patients who received transplant within 12months. Patient attending Mid-Hudson Valley transplant center Age 30-50+ Sample Exclusion Characteristics The exclusion characters of the participants are Patients who received transplant in more than 1yr. Patients not attending Mid-Hudson valley. Patients less than 30yrs. Data Collection Data will be collected using focus groups, semi-structured interviews. Semi-structured interviews will consist of five open-ended questions and 10 closed-ended questions. The open-ended questions will broadly cover medication adherence perceptions, medication side effects and the transplant experience. The close ended questions will broadly cover demographic information such as age, gender, income, and medical history. The interviews will be conducted by the researcher and audiotaped. The interviews will take place at the Mid-Hudson Valley transplant center in a private office. The interview will take approximately 60 minutes. Data Analysis Data will be analyzed using content analysis. Content analysis is a systematic and objective method used to analyze textual, visual, or audio data to identify patterns, themes, or meanings. This qualitative research technique allows researchers to interpret and quantify the presence of certain words, themes, or concepts within the data, providing insights into the communication and experiences of patients, healthcare providers, and other stakeholders. (Erlingsson & Brysiewicz, 2017) The interviews will be transcribed verbatim. Transcriptions will then be analyzed to extract the manifest and latent meanings of the data so that themes will be developed based on the data. Demographic data will be analyzed using descriptive statistics. Descriptive statistics refers to the methods used to summarize and describe the main features of a collection of data in a study. (Kaliyadan & Kulkarni, 2019). These statistics provide simple summaries about the sample and the measures. Descriptive statistics will be used to describe the average age of the participants. This data will also use frequency counts to describe their, adherence level, education level and income level. References Aberumand, B., Dyck, B. A., & Towheed, T. (2020). Identifying perceptions and barriers regarding vaccination in patients with rheumatoid arthritis: A Canadian perspective. International Journal of Rheumatic Diseases, 23(11), 1526-1533. https://doi.org/10.1111/1756-185X.13971 Bendersky, V. A., Saha, A., Sidoti, C. N., Ferzola, A., Downey, M., Ruck, J. M., ... & Levan, M. L. (2023). Factors impacting the medication "Adherence Landscape" for transplant patients. Clinical Transplantation, 37(6), e14962. https://doi.org/10.1111/ctr.14962 Posluszny, D. M., Bovbjerg, D. H., Syrjala, K. L., Agha, M., Farah, R., Hou, J. Z., ... & Dew, M. A. (2022). Rates and predictors of nonadherence to the post-allogeneic hematopoietic cell transplantation medical regimen in patients and caregivers. Transplantation and cellular therapy, 28(3), 165-e1. https://doi.org/10.1016/j.jtct.2021.11.020 Bendersky, V. A., Saha, A., Sidoti, C. N., Ferzola, A., Downey, M., Ruck, J. M., Vanterpool, K. B., Young, L., Shegelman, A., Segev, D. L., & Levan, M. L. (2023). Factors impacting the medication "Adherence Landscape" for transplant patients. Clinical Transplantation. https://doi.org/10.1111/ctr.14962 Chen, T., Wang, Y., Tian, D., Zhang, J., Xu, Q., Lv, Q., Li, X., & Wang, J. (2022). Follow-Up Factors Contribute to Immunosuppressant Adherence in Kidney Transplant Recipients. Patient Preference and Adherence, Volume 16, 2811-2819. https://doi.org/10.2147/ppa.s383243 Ganjali, R., Ghorban Sabbagh, M., Nazemiyan, F., Mamdouhi, F., Badiee Aval, S., Taherzadeh, Z., Heshmati Nabavi, F., Golmakani, R., Tohidinezhad, F., & Eslami, S. (2019). Factors Associated with Adherence to Immunosuppressive Therapy and Barriers in Asian Kidney Transplant Recipients. ImmunoTargets and Therapy, Volume 8, 53-62. https://doi.org/10.2147/itt.s212760 Kostalova, B., Mala-Ladova, K., Kubena, A. A., Horne, R., Dusilova Sulkova, S., & Maly, J. (2021). Changes in Beliefs About Post-Transplant Immunosuppressants Over Time and Its Relation to Medication Adherence and Kidney Graft Dysfunction: A Follow-Up Study. Patient Preference and Adherence, Volume 15, 2877-2887. https://doi.org/10.2147/ppa.s344878 Ozdemir Koken, Z., Sezer Ceren, R. E., Karahan, S., & Abbasoglu, O. (2023). Factors Affecting Immunosuppressive Medication Adherence in Liver Transplant Recipients with Poor Adherence: A Qualitative Study. Patient Preference and Adherence, Volume 17, 983-993. https://doi.org/10.2147/ppa.s398770 Posluszny, D. M., Bovbjerg, D. H., Syrjala, K. L., Agha, M., Farah, R., Hou, J.-Z., Raptis, A., Im, A. P., Dorritie, K. A., Boyiadzis, M. M., & Dew, M. A. (2021). Rates and Predictors of Non-adherence to the Post-Allogeneic Hematopoietic Cell Transplantation Medical Regimen in Patients and Caregivers. Transplantation and Cellular Therapy. https://doi.org/10.1016/j.jtct.2021.11.020 Yaira Hamama-Raz, Frishberg, Y., Menachem Ben-Ezra, & Levin, Y. (2023). The Interrelations of Family Relationship, Illness Cognition of Helplessness and Perceived Barriers to Medication Adherence: A Study of Adolescent and Emerging Adult Kidney Recipients and Their Parents. Adolescent Health, Medicine and Therapeutics, Volume 14, 205-215. https://doi.org/10.2147/ahmt.s423355 Zhang, P., X., Yan, J., & Liu, J. (2021). Identifying Immunosuppressive Medication Non-adherence Factors Through a Combined Theory Model in Renal Transplant Recipients. Frontiers in Pharmacology, 12.https://doi.org/10.3389/fphar.2021.655836 LaMorte, W. W. (2022, November 3). The Health Belief Model. Behavioral Change Models; Boston University School of Public Health. https://sphweb.bumc.bu.edu/otlt/MPH-Modules/SB/BehavioralChangeTheories/BehavioralChangeTheories2.html Bradshaw, C., Atkinson, S., & Doody, O. (2017). Employing a Qualitative Description Approach in Health Care Research. Global Qualitative Nursing Research, 4(1), 1-8. https://doi.org/10.1177/2333393617742282 Boyd, R., Powney, G. D., & O.L. Pescott. (2023). We need to talk about nonprobability samples. Trends in Ecology & Evolution, 38(6), 521-531. https://doi.org/10.1016/j.tree.2023.01.001 Palinkas, L., Horwitz, S., Green, C., Wisdom, J., Duan, N., & Hoagwood, K. (2015). Purposeful Sampling for Qualitative Data Collection and Analysis in Mixed Method Implementation Research. Administration and Policy in Mental Health and Mental Health Services Research, 42(5), 533-544. PubMed Central. https://doi.org/10.1007/s10488-013-0528-y Erlingsson, C., & Brysiewicz, P. (2017). A hands-on guide to doing content analysis. African Journal of Emergency Medicine, 7(3), 93-99. Sciencedirect. https://doi.org/10.1016/j.afjem.2017.08.001 Kaliyadan, F., & Kulkarni, V. (2019). Types of variables, Descriptive statistics, and Sample Size. Indian Dermatology Online Journal, 10(1), 82-86. ncbi. https://doi.org/10.4103/idoj.IDOJ_468_18

******CLICK ORDER NOW BELOW AND OUR WRITERS WILL WRITE AN ANSWER TO THIS ASSIGNMENT OR ANY OTHER ASSIGNMENT, DISCUSSION, ESSAY, HOMEWORK OR QUESTION YOU MAY HAVE. OUR PAPERS ARE PLAGIARISM FREE*******."