Anoshirvan Kazemnejad

Background: Justice and non‐discrimination are core principles of professional nursing ethics. Yet, growing evidence shows that discriminatory behaviours still occur in clinical environments. This study seeks to explore clinical nurses' experiences and perceptions regarding the factors that lead to discrimination in nursing care. Methods: This qualitative secondary analysis employed a conventional content analysis. The dataset comprised 13 in‐depth, semi‐structured interviews with clinical nurses working in public and private hospitals in Tehran, Iran. These data were originally collected between June 2023 and May 2024 for two primary studies by the same research team. Purposive sampling with maximum variation was used. The data analysis was conducted manually following the method outlined by Graneheim and Lundman. Trustworthiness was maintained by applying Lincoln and Guba's criteria, including member checking, peer debriefing, and external audit. Results: Three main categories of factors contributing to discrimination in nursing care were identified: (1) patient‐related factors (including social and economic status, demographic characteristics, clinical condition, and interaction and behavioural characteristics); (2) organizational factors (including hospital policies and guidelines, resource constraints, workload pressures, external influence or interference, and lack of formal education on discrimination and justice); and (3) nurse‐related factors (including receipt of informal benefits and nurses' prior experiences and emotions). Conclusion: Discrimination in nursing care is a complex, systemic issue that results from the interactions between patient traits, organizational needs, and individual nurse factors. Addressing this effectively involves shifting from a solely individual approach to implementing organizational changes, alleviating systemic pressures, and offering focused education on justice and non‐discrimination within nursing practice. Implications for the Profession and/or Patient Care: The findings provide a multi‐level causal framework to guide interventions at the point of care, the unit level, and the educational level. Addressing organizational pressures, informal referrals, and educational gaps can reduce discriminatory practices and promote equitable, dignity‐conserving care. Impact: This study addresses the question of why discriminatory nursing care occurs, moving beyond descriptive accounts to identify actionable contributing factors. The findings benefit nurses, nursing managers, educators, and policymakers by providing an evidence base for targeted interventions. Reporting Method: The study was reported in accordance with the Consolidated Criteria for Reporting Qualitative Research (COREQ) checklist. Patient or Public Contribution: No patient or public contribution.

Background: Older adults often experience a wide range of physical, emotional, and social challenges as they age, requiring them to draw on internal strengths and coping resources. Among these resources, spiritual self-care has been recognized as an important pathway to achieving inner peace, acceptance, and emotional balance during difficult life experiences. Through spiritual self-care, older adults may find meaning in their struggles and develop a greater capacity to tolerate and adapt to life’s challenges. Objectives: This study aimed to explore the concept of spiritual self-care and to elucidate how it contributes to older adults’ tolerance and adaptation in the face of life challenges. Methods: This qualitative study was conducted in 2024 using a conventional content analysis approach. Seventeen older adults were recruited via purposive sampling. Data were collected through individual semi-structured interviews and analyzed concurrently with data collection using the conventional content analysis method described by Graneheim and Lundman. Results: Analysis of participants’ experiences identified five main themes describing how older adults cope with life challenges: “Acceptance and Meaning-Making of Suffering,” “Spiritual Self-Care and Emotional Regulation,” “Distancing from Sources of Tension,” “Positive Attitude and Gratitude,” and “Perceived Divine Support.” The findings indicated that older adults relied on a combination of spiritual, emotional, and social resources to navigate difficult experiences. Spiritual self-care emerged as a fundamental aspect of this process, helping participants maintain emotional stability, find meaning in suffering, and sustain a sense of hope and inner strength. Conclusions: The findings indicate that spiritual self-care can help older adults face life difficulties by facilitating acceptance, personal growth, and a stronger spiritual connection with God. These experiences may enable older adults to approach challenges with greater peace, resilience, and emotional balance. Incorporating spiritual dimensions into care planning for older adults may provide a more comprehensive approach to promoting their psychological well-being.

Although knowledge, attitude, practice, and self-efficacy are recognized as important determinants of cervical cancer screening behavior, evidence examining their combined influence on Pap smear testing adherence remains limited. This cross-sectional study of 985 women aged 30–59 years in Saveh, Iran, used two-stage sampling. Validated questionnaires assessed KAPSE and PST adherence intent. Mann–Whitney U, Kruskal–Wallis, and Spearman correlation tests were used. Linear regression evaluated a composite KAPSE variable’s impact on screening adherence. Participants had a median age of 40 years (IQR: 34–47). Most were housewives (79.3%) and reported below-average economic status (51.7%). Median (IQR) scores were 6 (4–8) for knowledge, 39 (36–42) for attitude, 4 (3–4) for practice, 31 (25–34) for self-efficacy, and 66 (56–74) for PST adherence intent. Self-efficacy demonstrated the strongest positive correlation with adherence (r = 0.597, p < 0.001). Regression analysis showed that the composite KAPSE score was a significant predictor of adherence; a one-unit increase in the KAPSE index was associated with a 7.2-unit increase in PST adherence (B = 7.20, t = 27.69, p < 0.001). Education level and previous screening history were the strongest demographic determinants (p < 0.001). Self-efficacy and prior practice are critical drivers of screening adherence. Health promotion programs should move beyond simple knowledge transfer to build women’s confidence and address socioeconomic barriers. Culturally tailored interventions that strengthen KAPSE synergy can significantly improve cervical cancer prevention outcomes in Iran. Clinical trial number: IRCT20231130060228N1. Registration date: 2024-02-19, 1402/11/30. URL: https://irct.behdasht.gov.ir/trial/74165.

Background: Discriminatory nursing care endangers patient rights, ethical standards and fair access to treatment—yet the lived experiences of nurses who engage in such practices remain understudied. This study seeks to explore Iranian nurses' personal experiences with their own discriminatory behaviours towards vulnerable patients in everyday clinical settings. Methods: A qualitative conventional content analysis was conducted in 2024, using in‐depth, unstructured interviews with 13 nurses from both public and private hospitals in Iran. Participants were purposefully sampled to ensure maximum variation, and data collection continued until saturation was reached. The interviews were analysed using the five‐step framework developed by Graneheim and Lundman. Results: A central theme, ‘discrimination in service provision for vulnerable patients’, emerged, highlighting two main categories: (1) Professional Shortfalls in the Care of Older Adults and (2) Shortfalls in Care for Persons at the End of Life. Nurses reported prioritizing younger patients, delaying or withholding essential care, reducing therapeutic communication and demonstrating inconsistent adherence to professional standards. Conclusion: Findings reveal subtle yet widespread discriminatory practices against older adults and end‐of‐life patients. Combating these disparities requires targeted education, supportive organizational policies and strategies to increase nurses' awareness of unconscious bias. Implications for Practice: Reducing discriminatory behaviours promotes equitable, person‐centred care. Enhancing empathy, prioritizing the needs of vulnerable patients and strengthening adherence to professional standards foster trust, dignity and a more inclusive clinical environment.

Aim: This study aimed to explore and clarify the concept of professional engagement among clinical nurses. Background: Nurses are central to shaping healthcare systems through direct interactions with patients and communities. Despite their potential to influence health policy, their involvement in policy development remains limited, especially in culturally specific contexts like Iran. Understanding the nature of professional engagement is essential for strengthening nursing practice and informing policy. Methods: This qualitative descriptive study employed a conventional content analysis approach. Data were collected through semi‐structured interviews with 16 clinical nurses selected via purposive sampling to ensure diversity in hospital type, ward specialty, and geographic location across Iran. Results: The analysis yielded four main categories: benevolence, vigor, professional emotional belonging, and meaningful care. They reflect the humanistic, energetic, emotionally connected, and purpose‐driven nature of nursing practice. Discussion: Professional engagement in nursing is shaped by intrinsic motivation, emotional connection, and a strong sense of purpose. These factors contribute to nurses’ commitment and the quality of care they provide. Conclusion: Professional engagement in nursing is defined by intrinsic satisfaction in helping others, emotional attachment to the profession, and a strong sense of purpose. These are features that distinctly set nursing apart from other professions. Implications for Nursing: Supporting nurses’ emotional connection, teamwork, and sense of purpose can strengthen professional engagement. Nurse managers should foster reflective practice and meaningful caregiving to improve work motivation and care quality. Implications for Nursing Policy: Policymakers should implement strategies that reinforce engagement across workshifts. Policy‐driven efforts can enhance patient safety, care quality, and nurse retention at various caring levels.

Campylobacteriosis is a major cause of gastroenteritis globally, but comprehensive data on its prevalence and antimicrobial resistance (AMR) patterns in Ir

Cervical cancer (CC) is a significant global health concern, particularly in low- and middle-income countries, where screening participation remains low. T

Background: Cervical cancer is the leading malignancy among women worldwide, posing clinical and public health challenges. This in silico study aims to identify potential diagnostic biomarkers, therapeutic targets, and prognostic markers associated with cervical cancer through integrative bioinformatics approaches. Methods: A hybrid machine learning approach, combining genetic algorithm (GA) and support vector machine (SVM), was applied to high-dimensional gene expression data from publicly available transcriptomic datasets, including the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). A total of 72 Geo samples (Affymetrix, Illumina) served as the primary dataset after normalization. Results: The GA-SVM model achieved about 99% accuracy and AUC with 10-fold cross validation, clearly separating cervical cancer from normal tissues. Eight genes (CXCL9, CTGF, ZNF704, ZEB2, SASH1, PTN, KPNA2, SLC5A1) were identified as diagnostic biomarkers. Protein-protein interaction (PPI) and functional enrichment analyses revealed 42 therapeutic targets (e.g. CDK1, BRCA1, CCNB1, and AURKB) linked to regulating cell cycle, DNA repair, and mitotic processes. Survival analysis identified six genes (CXCL1, DNMT1, MMP1, MYBL2, PCNA, and RRM2) as key prognostic markers. Additionally, transcription factor analysis identified E2F1 and TP63 as major regulators of the prognostic genes, elucidating the molecular mechanisms underlying cervical cancer progression. Conclusion: The identified gene signatures may serve as candidates for hypothesis generation and provide a computational framework to prioritize biomarkers and therapeutic targets in cervical cancer. However, these findings are based on in silico analyses and require experimental and clinical validation before translation into practice.

This research uses machine learning (ML) models to determine significant predictive factors for CVD events in Iran, a country with high mortality rates. The study evaluates the effectiveness of deep learning and mixed-effects logistic models in predicting 10-year CVD incidence using longitudinal TLGS data. A total of 4,872 adults aged ≥ 30 years without a history of CVD at baseline (2006–2008) were followed until 2020. Following exclusions due to prevalent CVD or insufficient follow-up data, the final analytic sample comprised 1,942 men and 2,930 women. Baseline demographic, behavioral, and biochemical characteristics were utilized as input features. A clinical history examination was employed to identify and confirm incident CVD events, such as stroke and coronary heart disease (CHD). Using longitudinal data collected during a 10-year study period, deep learning models based on Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures were utilized to detect variations in risk factor dynamics. The area under the receiver operating characteristic curve (AUC) was employed to assess the model’s performance. During the follow-up period, 545 participants (11.2%) experienced CVD events. In both sexes, the GRU model outperformed the LSTM model. In women, the GRU achieved an AUC of 0.739, whereas in men it achieved 0.738, in both cases outperforming LSTM. Compared with the traditional mixed-effects model, discrimination was comparable in women (GRU 0.739 vs. 0.74) and higher in men (0.738 vs. 0.70). Even though the models were restricted to 21 commonly employed clinical variables, their performance was similar to that of larger-scale studies that included hundreds of variables. Deep learning models, such as GRU, can effectively leverage longitudinal medical data to predict future cardiovascular disease events. The models achieved performance comparable to studies that used far more variables, despite relying on a limited feature set.