Background: COVID-19 represents a threat both for the physical and psychological health of oncological patients experiencing heightened distress levels to which the fear of the virus is also added. Moreover, fear of COVID-19 could lead oncological patients to experience feelings of hopelessness related to their medical care. Patient-centered communication may act as a buffer against the aforementioned variables. This study aimed to test the role of doctor–patient communication in the relationship between fear of COVID-19 and hopelessness. Methods: During the COVID-19 pandemic, a sample of 90 oncological outpatients was recruited (40 males (44.4%) and 50 females (55.6%), mean age = 66.08 (SD = 12.12)). A structured interview was developed and used during the pandemic to measure the patients’ perceived (A) fear of COVID-19, and (B) feelings of hopelessness, and (C) physicians’ use of empathetic and (D) clear language during the consultation. A multiple mediation model was tested, and the effects between males and females were also compared. Results: Empathetic and clear doctor–patient communication buffered the adverse effect of the fear of COVID-19 on hopelessness through a full-mediation model. The effects did not differ between males and females in the overall model but its indirect effects. Discussions: Patient-centered communication using empathy and clear language can buffer the adverse effect of the fear of COVID-19 and protect oncological patients from hopelessness during the pandemic. These findings might help to improve clinical oncological practice.
BackgroundPsychological research in oncological settings is steadily increasing and the construct of psychological distress has rapidly gained popularity—leading to the development of questionnaires aimed at its measurement. The Psychological Distress Inventory (PDI) is one of the most used instruments, but its psychometric properties were not yet deeply evaluated. The present studies aimed at investigating the psychometric properties of the PDI (Study 1) and providing a revised version of the tool (Study 2).MethodsOncological outpatients were enrolled at the Department of Medical Oncology of the Presidio Ospedaliero of Saronno, ASST Valle Olona, Italy. For the first study (N = 251), an Exploratory Graph Analysis was used to explore the item structure of the PDI. In the second study (N = 902), the psychometric properties of the revised PDI (PDI-R) were deeply assessed.ResultsStudy 1 showed that the PDI has a not clear structure and it should be reconsidered. On the opposite, Study 2 showed that the revised version (PDI-R) has a solid factorial structure, it is invariant across gender and age, and it has good psychometric properties.ConclusionResults suggest that the PDI-R is a reliable measure of psychological distress in different samples of oncological patients, with stronger psychometric properties than the original version. Its use in the clinical and research field is therefore recommended to improve the quality of both assessment and treatment of psychological distress in patients with oncological problems.
e24128 Background: In recent years, an ever greater importance is given to the needs of cancer patients which could impact medical treatments, adherence and compliance, and patients’ quality of life (Teo et al. 2019; NCCN, 2015). However, recognizing and addressing the needs of patients may not be enough. Indeed, on one hand, needs that clinicians might consider as 'important' could play a marginal role, and – on the other hand – needs that clinicians may perceive as 'unimportant' may be central for patients. An innovative approach – psychometric network analysis (PNA; Epskamp, 2017) – was used to assess the network among needs ( nodes). Notably, the more a central node (need) is modified (addressed), the more a cascade change will occur in all the other nodes (needs). This study aimed to evaluate: (A) the structure of relationships among needs ( edges) of cancer patients, (B) which needs ( nodes) are the most relevant ( central), and (C) play a key role in the network. Methods: Patients ( n = 511; mean age = 65.95, SD = 12.72; 280 males) were enrolled at the Oncology Day Hospital at the “Presidio Ospedaliero” of Saronno, ASST Valle Olona, Italy. Patients were tested with the Need Evaluation Questionnaire (NEQ) which is composed of 23 items that investigate as many needs – divided into 4 areas: (1) information about diagnosis/prognosis, (2) information about exams and treatment, (3) communicative, and (4) relational needs. Results: Preliminary analysis revealed that all of the items were informative (SDitem < 2.5SDall_itemsSD) and there was no redundancy between items (redundancy index < 0.25). An Ising model (5,000 nonparametric bootstraps) with LASSO regularized nodewise logistic regression was performed. PNA showed a high accuracy: CS-coefficient = 0.56. On one hand, PNA showed that item#2 ( “I need more information about my future condition”; z = 2.039), item#17 ( “I need to speak with a psychologist”; z = 1.209), and item#13 ( “I need to be reassured more by the doctors”; z = 1.201) were the strongest (central) nodes. On the other hand, item#14 ( “ I need the hospital to provide better services”; z = -2.261) was the weakest node in the network. Conclusions: These findings show useful implications for clinical practice. Clinical interventions should address the needs showing the strongest connections in the network. These central nodes can influence all the other connected needs, thus representing important needs to be targeted by clinicians – allowing to tailoring more targeted and efficient therapeutic approaches to meet patients’ needs, with beneficial effects for medical treatments and quality of life.
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