Objective: This study aimed to examine the effectiveness of Parent Management Training on oppositional behaviors, parenting stress, and parent–child interaction in children with attention-deficit/hyperactivity disorder.

Methods and Materials: This quasi-experimental study was conducted using a pre-test, post-test, and follow-up design with an experimental group and a control group. The statistical population consisted of children with ADHD and their parents in Tehran, Iran. A total of 30 parent–child dyads were selected through purposive sampling and randomly assigned to the experimental group and control group, with 15 dyads in each group. The experimental group received Parent Management Training in 10 weekly sessions, each lasting approximately 90 minutes, while the control group remained on the waiting list and received no structured intervention during the study period. Data were collected using standardized measures of oppositional behaviors, parenting stress, and parent–child interaction at three stages: pre-test, post-test, and follow-up. The data were analyzed using repeated-measures analysis of variance and Bonferroni post hoc tests.

Findings: The results showed significant time, group, and time × group interaction effects for all study variables. The interaction effect of time and group was significant for oppositional behaviors (F = 47.29, p < .001, η² = .628), parenting stress (F = 36.21, p < .001, η² = .564), and parent–child interaction (F = 34.56, p < .001, η² = .552). Bonferroni comparisons indicated that oppositional behaviors and parenting stress decreased significantly from pre-test to post-test and from pre-test to follow-up in the experimental group, while parent–child interaction increased significantly across the same stages. No significant differences were observed between post-test and follow-up scores, indicating stability of intervention effects.

Conclusion: Parent Management Training was effective in reducing oppositional behaviors and parenting stress and improving parent–child interaction in children with ADHD, and its effects remained stable during the follow-up period.

Objective: This study aimed to explore the experiences of meaning-making, illness acceptance, and quality of life among patients living with advanced cancer in Canada.

Methods and Materials: This qualitative inquiry was conducted among 26 adult patients diagnosed with advanced or metastatic cancer who were receiving oncology or palliative care services in Canada. Participants were selected through purposive sampling to ensure variation in age, gender, cancer type, treatment status, and care setting. Data were collected through semi-structured interviews, a demographic and clinical information form, and researcher field notes. The interviews explored patients’ understanding of illness, experiences of acceptance, sources of meaning, perceived changes in identity and relationships, and definitions of quality of life during advanced disease. Interviews were audio-recorded, transcribed verbatim, and analyzed using thematic analysis. Coding was performed through repeated reading, identification of meaning units, development of initial codes, formation of categories, and extraction of overarching themes. Credibility, dependability, confirmability, and transferability were supported through peer discussion, reflexive memo writing, an audit trail, and detailed description of the research context.

Findings: The analysis identified three major themes. The first theme, reconstructing meaning in the face of advanced illness, showed that participants attempted to preserve identity, reinterpret life priorities, confront mortality, and find coherence through relationships, spirituality, contribution, and reflection. The second theme, negotiating acceptance without surrender, indicated that illness acceptance was not experienced as giving up, but as a fluctuating process of living with uncertainty, maintaining agency, balancing hope and realism, and preparing emotionally and practically for the future. The third theme, redefining quality of life in advanced cancer, revealed that participants associated quality of life with relief from physical suffering, dignity, relational closeness, normalcy in daily life, freedom from being a burden, and peace in the present.

Conclusion: The findings suggest that meaning-making, illness acceptance, and quality of life are reciprocal and dynamic processes among patients with advanced cancer. Patient-centered palliative oncology should therefore address existential meaning, emotional adjustment, dignity, family concerns, symptom relief, and personal definitions of quality of life.

Objective: This study aimed to examine the relationship between perceived social support and quality of life among older adults with chronic disease and to test the mediating roles of loneliness and depressive symptoms in this relationship.

Methods and Materials: This cross-sectional correlational study was conducted among 426 older adults with chronic disease in Canada. Participants were recruited from outpatient chronic disease clinics, community health centers, senior service organizations, and primary care referral networks. Eligible participants were aged 65 years or older and had at least one physician-confirmed chronic disease. Data were collected using a demographic and clinical information form, the Multidimensional Scale of Perceived Social Support, the UCLA Loneliness Scale, the 15-item Geriatric Depression Scale, and the WHOQOL-OLD. Data were analyzed using descriptive statistics, Pearson correlation analysis, and structural equation modeling. The indirect effects were examined using bootstrapping with 5,000 resamples and 95% confidence intervals.

Findings: Pearson correlation analysis showed that perceived social support was negatively correlated with loneliness (r = -0.58, p < 0.001) and depressive symptoms (r = -0.46, p < 0.001), and positively correlated with quality of life (r = 0.52, p < 0.001). Loneliness was positively correlated with depressive symptoms (r = 0.63, p < 0.001) and negatively correlated with quality of life (r = -0.61, p < 0.001). Depressive symptoms were also negatively correlated with quality of life (r = -0.66, p < 0.001). The structural model showed good fit to the data: χ²/df = 2.23, CFI = 0.972, TLI = 0.958, GFI = 0.965, RMSEA = 0.054, and SRMR = 0.041. Perceived social support had significant direct effects on loneliness (β = -0.58, p < 0.001), depressive symptoms (β = -0.15, p = 0.002), and quality of life (β = 0.19, p < 0.001). Loneliness predicted depressive symptoms (β = 0.54, p < 0.001) and quality of life (β = -0.24, p < 0.001), while depressive symptoms predicted quality of life (β = -0.42, p < 0.001). Bootstrapping confirmed significant indirect effects through loneliness, depressive symptoms, and their sequential pathway.

Conclusion: Perceived social support was directly and indirectly associated with better quality of life among older adults with chronic disease, and loneliness and depressive symptoms partially mediated this relationship.

Objective: This study aimed to explore the perceived determinants of quality of life among women with breast cancer after completion of radiotherapy.

Methods and Materials: This qualitative study was conducted using a conventional content analysis approach. Participants were 24 women with breast cancer in Canada who had completed radiotherapy and were recruited through purposive sampling with maximum variation in age, marital status, educational level, employment status, cancer stage, treatment history, and time elapsed since radiotherapy. Data were collected through semi-structured individual interviews, a demographic and clinical information form, and field notes. Interviews focused on women’s perceptions of physical, emotional, social, relational, healthcare-related, and existential factors affecting their quality of life after radiotherapy. All interviews were audio-recorded, transcribed verbatim, and analyzed using conventional qualitative content analysis. Meaning units were identified, condensed, coded, compared, and organized into subcategories and main categories. Credibility, dependability, confirmability, and transferability were strengthened through prolonged engagement with the data, peer debriefing, reflexive memo writing, participant confirmation of selected interpretations, and maintenance of an audit trail.

Findings: The analysis revealed five main categories that explained the perceived determinants of quality of life after radiotherapy: physical recovery, emotional reconstruction, relational support, healthcare continuity, and restored personal agency. Physical recovery included fatigue, pain, skin and breast changes, sleep disturbance, and reduced physical capacity. Emotional reconstruction reflected fear of recurrence, uncertainty, altered self-confidence, emotional exhaustion, and gradual acceptance. Relational support included family understanding, partner communication, peer support, and reduced isolation. Healthcare continuity involved information, accessible follow-up, side-effect management, and trust in professionals. Restored personal agency referred to return to roles, self-care strategies, body acceptance, and redefinition of normal life.

Conclusion: Quality of life among women with breast cancer after radiotherapy is shaped by an interconnected recovery process involving physical symptoms, emotional adjustment, social support, healthcare responsiveness, and the reconstruction of personal control. Post-radiotherapy survivorship care should therefore extend beyond clinical monitoring and include comprehensive physical, psychological, relational, informational, and practical support.

Objective: This study aimed to develop and interpret an explainable XGBoost model for predicting quality of life among patients with fibromyalgia based on pain intensity, sleep quality, psychological flexibility, and relevant demographic and clinical variables.

Methods and Materials: This cross-sectional predictive modeling study was conducted among 318 patients with fibromyalgia receiving services from rheumatology and pain management clinics in Tehran, Iran. Data were collected using a demographic and clinical information form, the Short Form Health Survey for quality of life, the Visual Analog Scale for pain intensity, the Pittsburgh Sleep Quality Index for sleep quality, and the Acceptance and Action Questionnaire-II for psychological flexibility. After data screening and preprocessing, quality of life was considered the continuous outcome variable. Predictive modeling was performed using XGBoost regression, and model performance was compared with linear regression, support vector regression, and random forest regression. Model accuracy was evaluated using mean absolute error, root mean square error, coefficient of determination, and cross-validation. SHAP analysis was used to explain the final XGBoost model.

Findings: Correlation analysis showed that quality of life was significantly and negatively associated with pain intensity (r = -0.58, p < 0.01) and poor sleep quality (r = -0.52, p < 0.01), while it was significantly and positively associated with psychological flexibility (r = 0.49, p < 0.01). The XGBoost model demonstrated the strongest predictive performance, with a test MAE of 7.84, test RMSE of 10.18, test R² of 0.66, and cross-validated R² of 0.63. SHAP analysis identified pain intensity as the most influential predictor, followed by poor sleep quality and psychological flexibility.

Conclusion: The findings indicated that quality of life among patients with fibromyalgia can be predicted with acceptable accuracy using an explainable XGBoost model, with pain intensity, sleep quality, and psychological flexibility emerging as the most important predictors.

Objective: This study aimed to develop a grounded theory explaining how adults with uncontrolled hypertension perceive and negotiate self-care, treatment burden, and quality of life.

Methods and Materials: This qualitative study was conducted using a grounded theory design among 34 adults with uncontrolled hypertension in Ontario, Canada. Participants were recruited from primary health care clinics, community health centers, and outpatient cardiovascular services through purposive and theoretical sampling. Eligibility criteria included being 30 years of age or older, having a diagnosis of hypertension for at least one year, and having uncontrolled blood pressure based on recent clinical records. Data were collected through semi-structured individual interviews, demographic and clinical information forms, and researcher field notes. Interviews were audio-recorded, transcribed verbatim, and analyzed concurrently with data collection using constant comparative analysis, including initial coding, focused coding, axial coding, and theoretical integration. Recruitment continued until theoretical saturation was achieved.

Findings: The analysis generated one core category, “negotiating control under cumulative treatment burden,” and five main categories: living with an invisible but threatening condition, translating medical advice into daily self-care, carrying the burden of treatment, quality of life under pressure, and reconstructing control through practical adaptation. The findings indicated that participants experienced uncontrolled hypertension as a condition marked by uncertainty, emotional strain, and repeated self-care demands. Treatment burden emerged through the accumulation of medication routines, lifestyle restrictions, home monitoring, appointments, costs, information management, and relational pressures. Participants’ quality of life was affected by worry, guilt, reduced confidence, social restriction, and diminished spontaneity. However, participants also developed adaptive strategies, including selective adherence, family-supported routines, trigger recognition, and realistic self-care prioritization.

Conclusion: Adults with uncontrolled hypertension experience self-care as a continuous negotiation between clinical expectations and everyday capacity. Hypertension care should therefore address treatment burden, emotional distress, feasibility of self-care behaviors, and quality of life alongside blood pressure control.

Objective: This study aimed to examine the relationship between illness perception and health-related quality of life among patients with type 2 diabetes and to determine the mediating role of self-care behaviors in this relationship.

Methods and Materials: This descriptive-correlational cross-sectional study was conducted among 412 patients with type 2 diabetes receiving outpatient diabetes care in Canada. Participants were selected through convenience sampling based on inclusion and exclusion criteria. Data were collected using a demographic and clinical information form, the Brief Illness Perception Questionnaire, the Summary of Diabetes Self-Care Activities measure, and the Diabetes Quality of Life Brief Clinical Inventory. Data analysis was performed using SPSS and AMOS. Pearson correlation coefficients were used to examine associations among the main variables, and structural equation modeling was applied to test the proposed mediation model. The indirect effect was examined using the bootstrapping method with 5,000 resamples and 95% confidence intervals.

Findings: The results showed that illness perception was significantly and negatively correlated with self-care behaviors and health-related quality of life. Self-care behaviors were significantly and positively correlated with health-related quality of life. The structural model showed acceptable fit indices, including χ²/df = 1.79, CFI = 0.982, TLI = 0.973, GFI = 0.976, RMSEA = 0.044, and SRMR = 0.036. Illness perception had a significant negative direct effect on self-care behaviors and health-related quality of life, while self-care behaviors had a significant positive direct effect on health-related quality of life. The indirect effect of illness perception on health-related quality of life through self-care behaviors was also significant, confirming partial mediation.

Conclusion: The findings indicate that negative illness perception is associated with poorer health-related quality of life both directly and indirectly through reduced self-care behaviors. Interventions that improve illness perception and strengthen diabetes self-care may enhance quality of life among patients with type 2 diabetes.

Objective: This study aimed to predict quality of life among patients with chronic obstructive pulmonary disease using Random Forest and Gradient Boosting algorithms and to identify the most important clinical, respiratory, psychological, and comorbidity-related predictors of health-related quality of life.

Methods and Materials: This descriptive, cross-sectional, predictive study was conducted among 342 patients with chronic obstructive pulmonary disease who were selected from pulmonary clinics and hospital-affiliated respiratory care units in Arak, Iran. Eligible participants had a confirmed diagnosis of chronic obstructive pulmonary disease and completed standardized clinical and patient-reported measures. Health-related quality of life was assessed using the St. George’s Respiratory Questionnaire. Predictor variables included demographic characteristics, disease duration, spirometric indices, exacerbation and hospitalization history, COPD Assessment Test score, modified Medical Research Council dyspnea score, anxiety and depression scores, body mass index, and comorbidity burden. Data were analyzed using descriptive statistics, correlation analysis, and supervised machine learning regression. The dataset was divided into training and testing subsets, and Random Forest and Gradient Boosting models were developed using cross-validation and hyperparameter tuning.

Findings: Significant associations were found between poorer quality of life and higher COPD Assessment Test score, dyspnea severity, exacerbation frequency, hospitalization history, anxiety, depression, disease duration, body mass index, and comorbidity burden. Pulmonary function indices, including FEV1 percentage predicted, FVC percentage predicted, and FEV1/FVC ratio, were negatively associated with total quality-of-life score. In the independent testing set, the Random Forest model achieved a mean absolute error of 7.62, root mean square error of 9.81, and coefficient of determination of 0.64. The Gradient Boosting model demonstrated superior performance, with a mean absolute error of 6.93, root mean square error of 9.12, and coefficient of determination of 0.68. The most important predictors were COPD Assessment Test score, dyspnea severity, exacerbation frequency, depression, and FEV1 percentage predicted.

Conclusion: The findings indicate that machine learning algorithms, particularly Gradient Boosting, can predict quality of life among patients with chronic obstructive pulmonary disease with acceptable accuracy. Quality-of-life impairment was best predicted through a multidimensional profile combining symptom burden, dyspnea, exacerbation history, psychological distress, and pulmonary function.