Segundo Jiménez-García

Pain during the menopausal transition can be normalized, minimized or attributed prematurely, including to stress or psychological distress, raising questions of patient credibility and possible sex- and gender-related bias in clinical interpretation. This scoping review mapped pain recognition, attribution, assessment and care during the transition, interpreting the evidence primarily through a lens of patient credibility and gender bias, with mental-health-informed and interdisciplinary implications considered alongside. The protocol was deposited on the Open Science Framework. Following JBI guidance and PRISMA-ScR, two human reviewers independently screened records from eight databases searched from inception to 30 June 2026; final decisions were concordant. Data were charted and analysed using hybrid deductive–inductive descriptive qualitative content analysis. Of 8938 records, 4856 were screened, 43 reports assessed and 21 sources of evidence included, representing 23 studies. Most evidence was qualitative (n = 15), and pain was usually secondary or incidental. Musculoskeletal pain (n = 12), headache or migraine (n = 9), and sexual or genitourinary pain (n = 5) predominated. Six categories addressed recognition and attribution, normalization and legitimacy, clinical response, delay and misframing, inequality, and transferable implications for mental-health-informed interdisciplinary care. The evidence identifies the risks of both diagnostic omission and premature attribution but cannot show that all pain is menopause-related, quantify diagnostic bias or delay, or establish epistemic injustice as a mechanism. Assessment should include psychological wellbeing without allowing distress, a mental-health label, or an assumption about reduced credibility to substitute for physical differential diagnosis, referral and follow-up. No included study primarily examined mental-health services or tested a mental-health-specific intervention.

Background: Digital transformation is reshaping health care systems and requires health care professionals to develop advanced digital health competencies. The integration of AI into clinical practice introduces new demands related to critical evaluation, human oversight, ethical responsibility, and regulatory compliance. However, evidence linking validated measures of digital health competence with health care professionals’ attitudes toward AI remains limited. Objective: This study aimed to (1) assess digital health competence among health care professionals in Spain, (2) examine organizational conditions supporting competence development, (3) explore perceptions about AI use in the workplace, and (4) examine the association between digital health competence and attitudes toward AI. Methods: A national cross-sectional convergent mixed methods study was conducted between November 2023 and January 2024 with a voluntary convenience sample of 229 health care professionals. Digital health competence was assessed using DigiHealthCom (digital health competence instrument; 42 items, 5 domains) and DigiComInf (the aspects associated with digital health competence instrument; 15 items, 3 domains). Confirmatory factor analysis evaluated structural validity. Open-ended responses regarding AI perceptions were explored using inductive qualitative content analysis. AI attitudes were classified into 4 categories (positive, negative, ambivalent, and uncertain), and their association with digital health competence was examined using multinomial logistic regression adjusted for age and sex. Results: Participants were predominantly female (169/229, 73.8%) and nurses (123/229, 53.7%), with a mean age of 45.9 (SD 10) years. Overall digital health competence was moderate (mean 3.01, SD 0.51), with the highest scores in information and communication technology competence (3.34, SD 0.63) and the lowest in competence related to evaluating and implementing digital solutions (2.83, SD 0.63). Organizational and educational support for competence development was also moderate (mean 2.51, SD 0.57), with organizational planning receiving the lowest DigiComInf scores (2.21, SD 0.76). Confirmatory factor analysis supported the proposed factor structures for both instruments (DigiHealthCom: comparative fit index=0.95, root-mean-square error of approximation=0.049; DigiComInf: comparative fit index=0.95, root-mean-square error of approximation=0.084). Most participants expressed positive attitudes toward AI in the workplace (134/228, 58.77%). Qualitative findings revealed a pattern of conditional optimism, with expected benefits for efficiency and patient care balanced by concerns regarding training, governance, regulation, and human oversight. Conclusions: Digital health competence was positively associated with health care professionals’ attitudes toward AI. However, the cross-sectional design does not allow conclusions regarding the direction of this association. Deficiencies in higher-order competencies in evaluation and implementation, together with limited organizational support, highlight areas that may benefit from targeted educational and organizational strategies to promote the safe and responsible use of AI in health care.

Introduction: Machine learning (ML)-based prediction of opioid use disorder (OUD) in chronic non-cancer pain (CNCP) patients, may lead to early detection and intervention. However, most ML prediction studies are not based on prospective validations, limiting their potential to predict future new cases from opioid naïve patients. In the current study, we aimed to develop a multicenter and prospectively validate a ML model including sociodemographics, clinical, pharmacogenetic markers and sex differences.Methods: A predictive model was developed in real‑world European CNCP patients using a cohort of 713 patients and validated in an independent prospective cohort (n=143). Sociodemographic, clinical (VAS, EuroQol‑5D‑5L, HADS, MOS‑SS), pharmacological (daily morphine equivalent dose, opioid‑related adverse events), and genetic variables (CYP2D6 phenotype, OPRM1 A118G and COMT G472A genotypes) were collected. A Random Forest model was trained and its performance evaluated.Results: An OUD predictive model was developed to screen risk factors such as sex, younger age, high opioid dosage and adverse events. The model had perfect sensitivity, specificity and Kappa coefficient, and strong performance indicators in the validation. The timeframe of included patients (2011–2025) appeared to be the strongest predictor, suggesting the model may be capturing historical prescribing trends rather than universal clinical patterns. Conclusions: Our study presents an individualized prospective prediction of OUD cases by applying ML to multicenter CNCP real-world patients. Its novelty resides in integrating pharmacogenetics and sex differences. Such prospective predictions based on ML would be essential for future clinical validated and commercial applications for early detection of OUD.

Objective: To evaluate the mediating role of resilience in the relationship between burnout dimensions and the physical and mental health of nurses in December 2021 during the COVID‐19 pandemic. Design: Observational correlational study with a quantitative, cross‐sectional approach. Methods: A total of 256 nurses participated by completing an online survey in December 2021. Sociodemographic data were collected, and the Maslach Burnout Inventory–Human Services Survey, the Connor‐Davidson Resilience Scale, and the Short‐Form 12‐Item Health Survey version 2 were administered. Results: Resilience showed statistically significant indirect effects linking emotional exhaustion and depersonalisation to poorer mental health (negative indirect effects) and personal accomplishment to better mental health (positive indirect effect). Indirect effects involving physical health were smaller and less consistent: no indirect effect was observed for emotional exhaustion, whereas depersonalisation showed a small negative indirect effect and personal accomplishment showed a positive indirect effect through resilience. Resilience differed by sex and age, with men showing greater resilience. Conclusion: Findings are consistent with resilience being associated with mental health and statistically accounting for part of the association between burnout dimensions and mental health. Given the cross‐sectional design, causal direction cannot be inferred; resilience‐focused interventions should be evaluated in longitudinal and experimental studies. Implications for the Profession and Patient Care: Resilience is associated with better mental health and may represent a promising target for future intervention studies. Healthcare institutions could consider resilience‐support strategies alongside organisational measures to address burnout while evaluating their effectiveness using robust designs. Impact: What problem did the study address? It explored how resilience can mediate the relationship between burnout and the physical and mental health of nurses during the COVID‐19 pandemic. What were the main findings? Resilience statistically accounted for part of the association between burnout dimensions and mental health, while indirect associations involving physical health were more heterogeneous and dimension‐specific. Where and on whom will the research impact? The findings will impact nurses in the Valencian Community and can extend to nursing professionals in similar contexts, informing practices and policies to improve well‐being and patient care. Reporting Method: The EQUATOR STROBE guidelines for cross‐sectional observational studies were followed, ensuring the quality and transparency of the report. Patient or Public Contribution: No patient or public contributed to the design or conduct of the study, the analysis or interpretation of the data, or the preparation of the manuscript.

During the COVID-19 pandemic, nurses have had to face logistical problems related to critical changes in their work environment, the supply of personal protective equipment (PPE), and hard personal life decisions. This study aimed to investigate the changes in the working environment, PPE use, personal lives, and well-being as perceived by nurses in hospitals and primary care centers in Spain, during the COVID-19 pandemic. A descriptive cross-sectional survey study was conducted in April 2020, including 61 public and private hospitals, and 852 primary care centers. A total of 1296 nurses participated. The redeployment practice for organizational or vulnerability reasons was used by 26.4% of the participants. A total of 61.9% of the nurses doubled the time they used masks, and 8.3% of them could only replace the mask once a week. A total of 11.2% (n = 145) of the nurses left home to avoid infecting their family. Finally, 60.97% of the participants stated that their well-being was reduced compared to the state before the pandemic. Changes in the work environment, the use of PPE, and personal life may be related to the decrease in personal well-being perceived by the nurses.