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Learn more: PMC Disclaimer | PMC Copyright Notice Worldviews Evid Based Nurs . 2026 Apr 10;23(2):e70133. doi: 10.1111/wvn.70133 Search in PMC Search in PubMed View in NLM Catalog Add to search The Italian Organizational Culture and Readiness Scale for System‐Wide Integration of EBP (OCRSIEP) Scale: Psychometric Adaptation of an Existing Scale Daniele Napolitano Daniele Napolitano 1 CEMAD, IBD Unit, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Rome, Lazio, Italy Find articles by Daniele Napolitano 1 , Alessio Lo Cascio Alessio Lo Cascio 2 Direction of Health Professions, La Maddalena Cancer Center, Palermo, Sicily, Italy Find articles by Alessio Lo Cascio 2 , Mattia Bozzetti Mattia Bozzetti 3 Direction of Health Professions, ASST Cremona, Cremona, Lombardy, Italy Find articles by Mattia Bozzetti 3, ✉ , Felice Curcio Felice Curcio 4 Faculty of Medicine and Surgery, University of Sassari, Sassari, Sardinia, Italy Find articles by Felice Curcio 4 , Simone Amato Simone Amato 5 Azienda Ospedaliera San Camillo Forlanini, Cardiac Intensive Care Unit, Heart Transplant Centre and ECMO, Rome, Lazio, Italy Find articles by Simone Amato 5 , Roberta Guardione Roberta Guardione 6 Neonatal Care Unit, City of Health and Science University Hospital of Torino, Turin, Piedmont, Italy Find articles by Roberta Guardione 6 , Alessandro Stievano Alessandro Stievano 7 Department of Clinical and Experimental Medicine, University of Messina, Messina, Sicily, Italy Find articles by Alessandro Stievano 7 , Pasquale Iozzo Pasquale Iozzo 8 Department of Biomedicine and Prevention, University Tor Vergata, Rome, Lazio, Italy Find articles by Pasquale Iozzo 8 , Daniela D'Angelo Daniela D'Angelo 9 Centro Nazionale Clinical Governance Ed Eccellenza Delle Cure, Istituto Superiore Della Sanità, Rome, Lazio, Italy Find articles by Daniela D'Angelo 9 , Ippolito Notarnicola Ippolito Notarnicola 10 Department Medicine and Surgical, University of Enna “Kore”, Enna, Sicily, Italy Find articles by Ippolito Notarnicola 10 , Daniela Tartaglini Daniela Tartaglini 11 Department of Health Professions, Fondazione Policlinico Universitario Campus Bio‐Medico, Rome, Lazio, Italy 12 Research Unit of Nursing Science, Department of Medicine and Surgery, Campus Bio‐Medico University, Rome, Lazio, Italy Find articles by Daniela Tartaglini 11, 12 , Dhurata Ivziku Dhurata Ivziku 11 Department of Health Professions, Fondazione Policlinico Universitario Campus Bio‐Medico, Rome, Lazio, Italy 13 Department of Nursing, Faculty of Medical Sciences, AAB College, Pristina, Kosovo Find articles by Dhurata Ivziku 11, 13 Author information Article notes Copyright and License information 1 CEMAD, IBD Unit, Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Rome, Lazio, Italy 2 Direction of Health Professions, La Maddalena Cancer Center, Palermo, Sicily, Italy 3 Direction of Health Professions, ASST Cremona, Cremona, Lombardy, Italy 4 Faculty of Medicine and Surgery, University of Sassari, Sassari, Sardinia, Italy 5 Azienda Ospedaliera San Camillo Forlanini, Cardiac Intensive Care Unit, Heart Transplant Centre and ECMO, Rome, Lazio, Italy 6 Neonatal Care Unit, City of Health and Science University Hospital of Torino, Turin, Piedmont, Italy 7 Department of Clinical and Experimental Medicine, University of Messina, Messina, Sicily, Italy 8 Department of Biomedicine and Prevention, University Tor Vergata, Rome, Lazio, Italy 9 Centro Nazionale Clinical Governance Ed Eccellenza Delle Cure, Istituto Superiore Della Sanità, Rome, Lazio, Italy 10 Department Medicine and Surgical, University of Enna “Kore”, Enna, Sicily, Italy 11 Department of Health Professions, Fondazione Policlinico Universitario Campus Bio‐Medico, Rome, Lazio, Italy 12 Research Unit of Nursing Science, Department of Medicine and Surgery, Campus Bio‐Medico University, Rome, Lazio, Italy 13 Department of Nursing, Faculty of Medical Sciences, AAB College, Pristina, Kosovo * Correspondence: Mattia Bozzetti ( [email protected] ) ✉ Corresponding author. Revised 2026 Jan 17; Received 2025 Nov 12; Accepted 2026 Feb 27; Issue date 2026 Apr. © 2026 The Author(s). Worldviews on Evidence‐Based Nursing published by Wiley Periodicals LLC on behalf of Sigma Theta Tau International. This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. PMC Copyright notice PMCID: PMC13067802 PMID: 41960791 ABSTRACT Background Organizational culture and readiness are critical determinants of evidence‐based practice (EBP) implementation. The Organizational Culture and Readiness Scale for System‐Wide Integration of EBP (OCRSIEP), developed within the ARCC framework, is a validated tool to assess these dimensions, but no Italian version currently exists. Aim To translate, culturally adapt, and psychometrically validate the OCRSIEP and its short form in Italian. Methods A validation study was conducted, using exploratory and confirmatory factor analyses to derive and test the underlying model, followed by reliability testing with multiple indices and measurement invariance analyses. Results Data were collected from 405 Italian nurses. Factor analyses supported a 19‐item, six‐factor structure explaining 59.5% of the variance, with a second‐order factor indicating an overarching construct. The three‐item short form showed strong model fit and explained 67% of the variance. Subscales demonstrated acceptable‐to‐excellent reliability, and partial scalar invariance was established across public and private facilities. Linking Evidence to Action The Italian OCRSIEP scales are valid and reliable tools to assess organizational readiness for EBP implementation. They can guide leaders, educators, and researchers in monitoring, benchmarking, and advancing EBP–oriented system transformation within the Italian healthcare context. 1. Introduction The systematic implementation of Evidence‐Based Practice (EBP) is among the most effective strategies to improve care quality, patient safety, and healthcare sustainability (Melnyk et al. 2022 ). Yet, adoption remains low and uneven across organizations (Ivziku et al. 2025 ; Melnyk et al. 2016 ) due to barriers including limited leadership and cultural support, pre‐licensure and workplace training gaps, constrained access to resources, and misperceptions about EBP complexity and workload (Pendoni et al. 2024 ; Saunders et al. 2019 ). Organizational culture and readiness are key determinants of system‐level EBP uptake; supportive contexts foster continuous learning, peer support, access to scientific resources, competent mentorship, and transformational leadership (Cleary‐Holdforth et al. 2022 ; Hu et al. 2025 ; Wudu et al. 2024 ). To address these needs, the Advancing Research and Clinical Practice through Close Collaboration (ARCC) model was developed to guide organizational integration of EBP (Melnyk et al. 2017 ; Melnyk and Fineout‐Overholt 2022 ). Its first step is assessing culture and readiness with valid tools, notably the Organizational Culture and Readiness Scale for System‐Wide Integration of EBP (OCRSIEP) scale (Melnyk et al. 2010 ). The OCRSIEP scale identifies organizational strengths and opportunities to enhance consistent EBP use and has shown excellent psychometric properties (Cleary‐Holdforth et al. 2022 ; Hooge et al. 2022 ; Melnyk et al. 2016 , 2020 , 2022 ). In Italy, no validated OCRSIEP version currently exists, limiting EBP implementation and monitoring in a context where EBP is still developing (Bozzetti et al. 2026 ). This study therefore aims to translate, culturally adapt, and evaluate the psychometric properties of the Italian OCRSIEP to provide a valid tool for assessing organizational culture and readiness for system‐wide EBP integration. 2. Materials and Methods 2.1. Design This study employed a cross‐sectional design (January to September 2024) and was conducted in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines (Cevallos and Egger 2014 ). 2.2. Translation and Adaptation Process With the developers' permission (Melnyk et al. 2008 , 2021 ). translation and cross‐cultural adaptation followed established guidelines (Beaton et al. 2000 ) two independent forward translations, reconciliation, expert panel review ( n = 15), and two independent blinded back‐translations. The final Italian version was checked against the original for semantic and conceptual equivalence and authorized by the developers. 2.3. Sample, Setting and Data Collection A convenience sampling strategy was used. Registered nurses and nurse managers working in hospitals, community care, or independent practice across Italy were invited via institutional email lists, professional networks, and social media. Eligible participants were actively practicing and proficient in Italian; those who did not provide informed consent or had insufficient language proficiency were excluded. Data were collected anonymously online using Google Forms. 2.4. Instruments for Data Collection 2.4.1. Sociodemographics The initial section of the questionnaire collected sociodemographic data, including age (years), gender, job position, highest qualification, professional experience, primary work environment (e.g., hospital, research institute), institutional affiliation (e.g., public), and previous participation in EBP‐related training. 2.4.2. OCRSIEP Scales The OCRSIEP long form (Melnyk et al. 2010 ) includes 25 items rated on a 5‐point Likert scale (1 = “not at all” to 5 = ‘very much’); higher scores indicate a more supportive organizational culture for EBP. It has established face/content validity and high internal consistency (Cronbach's α > 0.85). The OCRSIEP short form (Melnyk et al. 2021 ) includes three items rated on a 5‐point Likert scale (1 = ‘strongly disagree’ to 5 = ‘strongly agree’). It shows good internal consistency ( α = 0.87) and convergent validity with long‐form correlations (Melnyk et al. 2021 ). 2.5. Data Analysis All analyses were conducted using R (4.5.0) (R Core Team, 2023). The analytic workflow and the purpose of each psychometric step are summarized in Figure 1 . FIGURE 1. Open in a new tab Psychometric workflow and purpose of each analytic step. The figure summarizes the sequence of analyses used to translate, adapt, and evaluate the OCRSIEP long and short forms in the Italian context; detailed results are reported in Tables 2 , 3 , 4 . I‐CVI, item‐level content validity index; S‐CVI, scale‐level content validity index; CFA, confirmatory factor analysis; BCFA, bayesian confirmatory factor analysis; KMO , Kaiser–Meyer–Olkin; EFA, exploratory factor analysis; MGCFA, multi‐group confirmatory factor analysis; CR, composite reliability; AVE, average variance extracted; S/ N , signal‐to‐noise ratio; ωH, omega hierarchical; ECV, explained common variance; PPP, posterior predictive p ‐value; DIC, deviance information criterion; WAIC, Watanabe–Akaike information criterion; LOO, leave‐one‐out cross‐validation; R̂, Gelman–Rubin convergence diagnostic. Content validity was assessed by n = 15 experts rating comprehensibility, relevance, and clarity (4‐point scale); Item‐level CVI (I‐CVI) and scale‐level CVI (S‐CVI) were computed and values ≥ 0.70 were considered acceptable (Polit et al. 2007 ). The original structure was first tested via Confirmatory Factor Analysis (CFA) showing inadequate fit ( χ 2 (252) = 1336.12, p < 0.001; CFI = 0.769; TLI = 0.747; RMSEA = 0.112 [0.106–0.118]; SRMR = 0.064); therefore, an exploratory‐confirmatory approach was used. The sample was randomly split (60/40) for Exploratory Factor Analysis (EFA) (maximum likelihood extraction, promax rotation) followed by CFA in an independent subsample. Factor number was guided by parallel analysis (PA), and item retention by loadings (> 0.30), cross‐loadings, and conceptual interpretability, after confirming sampling adequacy (KMO; Bartlett's test). A second‐order CFA was then tested to examine whether a higher‐order readiness factor accounted for shared variance across the first‐order dimensions. Model fit was evaluated using CFI/TLI, RMSEA, and SRMR, targeting conventional ‘good fit’ criteria (L. Hu and Bentler 1999 ) and improved only where theoretically justified by modification indices. Reliability was examined using multiple indices using standard cutoffs (Cronbach and Gleser 1964 ; Taber 2018 ). Known‐group measurement invariance across work setting, type of organization, and years of experience was examined with multi‐group CFA. Invariance decisions were based primarily on changes in fit indices and χ 2 difference tests. For the short form, Bayesian CFA and Bayesian invariance testing were conducted using the blavaan package (Gelman et al. 2013 ; Holtmann et al. 2016 ). Fit was assessed using the posterior predictive p ‐value (PPP), and convergence was verified via R̂ statistics (< 1.01). Model comparison across known groups followed a Bayesian invariance testing framework using PPP, Deviance Information Criterion (DIC), and, when available, Widely Applicable Information Criterion (WAIC) and Leave‐One‐Out cross‐validation (LOO) (Vehtari et al. 2017 ). 2.6. Post‐hoc Sample Size Calculation Post hoc power for the RMSEA close‐fit test (H0: RMSEA ≤ 0.05 vs. H1: RMSEA = 0.08) was 0.99 for the EFA model (df = 141; n = 243) and 0.97 for the CFA model (6 factors; df = 148; n = 162), indicating adequate power to detect deviations from close fit. 2.7. Ethical Considerations Ethical approval was obtained from the local Ethics Committee Territoriale Lazio Area 2 (Protocol number 30.23 CET 2 CBM). The study adhered to recognized ethical standards and the Declaration of Helsinki. Individuals provided electronic informed consent. 3. Results 3.1. Sample Characteristics Sample characteristics are reported in Table 1 . TABLE 1. Characteristics of the sample. Variable Category Age (mean, SD) 41.0 (10.9) Self‐identified Gender Male 126 (31.1%) Female 279 (68.9%) Highest Educational Qualification Regional course 25 (6.2%) Bachelor's degree 134 (33.1%) I‐level Master's degree 148 (36.5%) Master's degree 73 (18.0%) II‐level Master's degree 18 (4.4%) Doctorate (PhD) 7 (1.7%) Job Position Staff Nurse 320 (79.0%) Nurse manager 86 (21.0%) Years of Professional Experience ≤ 1 year 17 (4.2%) 2–5 years 111 (27.4%) 6–10 years 60 (14.8%) 11–15 years 61 (15.1%) 16–20 years 44 (10.9%) > 20 years 112 (27.7%) Work Environment Hospital 266 (65.7%) Research Institute 36 (8.9%) Community healthcare 77 (19.0%) Nursing home 17 (4.2%) Other 9 (2.2%) Institutional Affiliation Public facility 297 (73.3%) Private accredited facility 92 (22.7%) Other 16 (4.0%) Previous Training on EBP Yes 210 (51.9%) No 195 (48.1%) Open in a new tab 3.2. Content Validity For the short‐form OCRSIEP Scale, I‐CVI ranged from 0.92–1.00, yielding a S‐CVI = 0.95. The long‐form OCRSIEP Scale produced I‐CVI values between 0.58–1.00. Items 10, 11, 13, and 16, each with an I‐CVI = 0.58, were subsequently revised. The expert team revised the items linguistically and discussed their clarity and relevance to local practices, addressing any discrepancies arising from literal translations or cultural differences, while preserving the core principles of the original OCRSIEP scale. The overall S‐CVI for the long form was 0.83. 3.3. Exploratory Factor Analysis Prior to conducting EFA, adequacy was confirmed (KMO = 0.930, Bartlett's χ 2 (231) = 6734.28, p < 0.001). PA extracted 7 factors explaining 45.9% variance. Five items (11, 16, 17a, 17b, 17c) were removed due to low loadings or a single‐item factor (17b). The refined solution retained 19 items across 6 factors, explaining 45.6% variance (Table S1 ). 3.4. Confirmatory Factor Analyses For the long form, a six‐factor model (Figure 2 ) including 19 items was tested via CFA. The model exhibited an overall good fit to the data: χ 2 (148) = 313.10, p < 0.001; CFI = 0.969; TLI = 0.960; SRMR = 0.037; RMSEA = 0.052 (90% CI [0.044, 0.061]); robustRMSEA = 0.048. A second‐order model (Figure 3 ) showed equally good fit, supporting an overarching readiness construct. The six dimensions were labeled: Nurses Expertise in EBP (F1), Staff Commitment to EBP (F2), Knowledge and Resources (F3), Nurses Advanced Competencies in Research (F4), Organizational Support to EBP (F5), and Other Experts in EBP (F6), explaining 59.52% of total variance. FIGURE 2. Open in a new tab Standardized path diagram of the final six‐factor model ( n = 162). Latent factors are represented as circles and observed items as rectangles. Standardized factor loadings with standard errors (SE) are displayed on the paths from latent variables to observed indicators. Double‐headed dashed arrows between latent variables represent covariances. FIGURE 3. Open in a new tab Path diagram of the second‐order confirmatory factor analysis model for the OCRSIEP scale (19 items). The diagram illustrates the hierarchical structure of the OCRSIEP instrument, in which six first‐order latent factors (F1–F6) load onto a single higher‐order factor (G), interpreted as overall organizational readiness for evidence‐based practice implementation. Each observed item loads onto its respective first‐order factor, with standardized factor loadings and standard errors displayed on the paths. All parameter estimates are statistically significant at p < 0.001. Residual variances and selected item‐level covariances are included for model clarity. The short form OCRSIEP‐SF, single‐factor BCFA model (Figure 4 ) showed excellent fit to the data (PPP = 0.49). No issues with convergence were observed (R̂s < 1.01). The OCRSIEP‐SF explained 67.46% of the total item variance. FIGURE 4. Open in a new tab Bayesian confirmatory factor model for the OCRSIEP‐SF. Item 1. SF loading was fixed for identification purposes. Posterior means and 95% credible intervals are shown for free loadings. 3.5. Reliability Results indicated a strong general factor ωH = 0.79 and high total reliability for both scales (Table 2 ). The ECV (0.59) supported the use of a unidimensional total score, while retaining meaningful subscale interpretations. TABLE 2. Reliability scores. Scales and Dimensions Cronbach's alpha McDonald's omega (ω) Guttman's lambda6 AVE Composite reliability S/N ratio ECV OCRSIEP Long‐Form 0.790 * 0.59 Nurses Expertise in EBP (F1) 0.925 0.925 0.894 0.807 0.926 12.520 Staff Commitment to EBP (F2) 0.879 0.880 0.867 0.546 0.878 7.165 Knowledge and Resources (F3) 0.764 0.779 0.708 0.438 0.700 2.331 Nursed Advanced Competencies in Research (F4) 0.779 0.779 0.637 0.650 0.786 3.675 Organizational Support to EBP (F5) 0.847 0.848 0.809 0.559 0.835 5.060 Other Experts in EBP (F6) 0.813 0.813 0.685 0.679 0.809 4.224 OCRSIEP Short‐Form 0.749 0.681 0.861 6.193 0.67 Open in a new tab Note: AVE, average variance extracted; ECV, explained common variance; S/ N , signal to noise ratio. * ωH = Hierarchical ω. The intraclass correlation coefficient (ICC) was 0.82 for the long form and 0.90 for the short form (95% CI, 0.78–0.86), indicating temporal stability for both Italian OCRSIEP versions. 3.6. Invariances Measurement invariance across setting, agency type, and professional experience was tested using a sequence of increasingly constrained multi‐group models for the long form (Table 3 ) and the short form (Table 4 ). TABLE 3. OCRSIEP Measurement Invariance across known‐groups. Known‐Group Model χ 2 (df) CFI ΔCFI RMSEA ΔRMSEA SRMR Δ χ 2 (df) p Invariance supported Setting (Hospital vs Community Care) Configural 613.92 (310) 0.934 − 0.074 − 0.055 − − − Metric 635.59 (324) 0.933 0.001 0.073 0.001 0.059 19.84 (14) 0.135 Yes Scalar 657.56 (338) 0.932 0.001 0.072 0.001 0.060 21.39 (14) 0.092 Yes Agency (Public vs. Other) Configural 639.26 (310) 0.927 − 0.077 − 0.059 − − − Metric 658.60 (324) 0.926 0.001 0.076 0.001 0.059 18.44 (14) 0.187 Yes Scalar 682.90 (338) 0.924 0.002 0.075 0.001 0.061 23.79 (14) 0.049 Partial Partial Scalar 674.53 (332) 0.924 0.002 0.075 0.001 0.060 15.89 (8) 0.044 Partial Experience (≤ 10y vs > 10y) Configural 666.67 (310) 0.923 − 0.080 − 0.056 − − − Metric 677.95 (324) 0.924 0.001 0.078 0.002 0.059 8.04 (14) 0.887 Yes Scalar 689.89 (338) 0.925 0.001 0.076 0.002 0.060 10.08 (14) 0.756 Yes Open in a new tab Note: Dashes (−) indicate that values are not applicable for the initial configural model or not computed due to lack of comparison. Configural invariance indicates that the same factor structure (pattern of fixed and free loadings) holds across groups. Metric invariance constrains factor loadings to be equal across groups, supporting comparisons of associations (e.g., correlations/regressions) between latent factors and other variables. Scalar invariance additionally constrains item intercepts to equality, supporting comparisons of latent mean scores across groups. Partial scalar invariance indicates that scalar invariance was not fully met; a subset of intercept constraints was relaxed based on model diagnostics to achieve acceptable fit, allowing approximate latent mean comparisons with caution. Changes in fit indices (ΔCFI, ΔRMSEA) reflect differences relative to the immediately preceding model (configural → metric; metric → scalar). TABLE 4. OCRSIEP‐SF measurement invariance across known‐groups. Known‐Group Model PPP DIC WAIC LOO ΔDIC vs config. ΔWAIC ΔLOO Invariance supported Setting (Hospital vs. Community Care) Configural 0.509 3114.441 3117.522 3117.556 − − − − Metric 0.365 3115.774 3118.460 3118.750 1.333 +0.938 1.194 Yes Scalar 0.307 3114.687 − − +0.246 − − Partial Agency (Public vs. Other) Configural 0.512 3100.733 3102.671 3102.714 − − − − Metric 0.417 3100.928 3102.881 3102.951 +0.195 +0.210 +0.237 Yes Scalar 0.356 3099.870 − − −0.863 − − Partial Experience (≤ 10y vs. > 10y) Configural 0.369 2446.061 3226.393 3217.895 − − − − Metric 0.518 3120.345 3122.790 3122.871 − − − Yes Scalar 0.616 3116.723 − − −3.622 − − Partial Open in a new tab Note: PPP (Posterior Predictive p ‐value): A Bayesian measure assessing model fit; values close to 0.5 indicate good fit, while values near 0 or 1 suggest poor fit. DIC (Deviance Information Criterion): A model comparison index in Bayesian statistics; lower values indicate better model fit while accounting for model complexity. WAIC (Watanabe‐Akaike Information Criterion): An improvement over DIC, WAIC estimates out‐of‐sample predictive accuracy; lower values denote better fit. LOO (Leave‐One‐Out Cross‐Validation): A Bayesian method for estimating model predictive accuracy by iteratively omitting one observation; lower values indicate better performance. ΔDIC, ΔWAIC, ΔLOO: Differences in model fit indices relative to the best‐fitting model (i.e., the model with the lowest value on each criterion). Positive values indicate worse fit compared to the reference configuration. Dashes (−) indicate that values were either not computed or not interpretable due to model comparison limitations. For the Experience group, the configural model showed a possible convergence warning; hence, comparisons focused primarily on metric vs scalar. Scalar invariance between Public and Private institutions showed Δ χ 2 (14) = 23.79, p = 0.049. To address localized non‐invariance, we estimated a partial scalar model by freeing item intercepts for Items 3, 10, 14, 15a, 18, and 19. The partial scalar model showed acceptable overall fit (robust CFI = 0.924; robust RMSEA = 0.075; SRMR = 0.060), supporting its use for group comparisons. Latent mean comparisons showed that nurses in Public settings scored significantly lower across all six latent dimensions of the OCRSIEP. The Public group demonstrated significantly lower means on F1 (estimate −0.48, SE 0.12, p < 0.001), F2 (−0.31, SE 0.10, p = 0.002), F3 (−0.43, SE 0.09, p < 0.001), F4 (−0.33, SE 0.11, p = 0.003), F5 (−0.53, SE 0.12, p < 0.001), and F6 (−0.53, SE 0.12, p < 0.001), indicating systematically lower organizational readiness for EBP. In the tenure‐based configural model of the OCRSIEP‐SF, MCMC diagnostics indicated a lack of convergence in the group with more than 10 years of experience (e.g., R̂s > 3.5), therefore invariance should be interpreted cautiously. 4. Discussion This study aimed to validate the Italian OCRSIEP in both full and short forms. Findings demonstrated a robust factorial structure, good internal consistency, and measurement invariance across known groups. These results support the scale's utility in the Italian context and align with international efforts to employ validated measures of organizational readiness for EBP implementation in complex healthcare systems (Miake‐Lye et al. 2020 ). Our analyses supported a six–factor structure, with a second–order general factor in the full form explaining 59% of variance and one‐factor in the short form explaining 67% variance. These findings likely reflect contextual differences in managerial models and interprofessional dynamics within the Italian system, consistent with cross‐national work showing that EBP readiness is shaped by governance, power distribution, and professional leadership (A Steen and Stewart 2024 ; Shea et al. 2014 ). The three–item short form showed good adaptability to the Italian context. Compared to the original U.S. short form (Melnyk et al. 2021 ), BCFA supported a unidimensional structure with satisfactory reliability. The Italian version explained a larger proportion of variance, indicating greater precision in capturing organizational readiness locally. Its main advantage is usability in high‐demand clinical settings where time constraints limit administration of longer measures. The Italian long‐form adaptation differed from the original: items 11, 16, and 17a–c were not supported psychometrically (with 17b forming a single‐item factor) and were removed to improve construct stability and interpretability. Item 11 primarily reflects general computer proficiency rather than EBP‐specific culture/readiness; in Italy, digital literacy varies and advanced skills are not uniformly expected, while organizational access to databases/resources is already captured by other items (e.g., items 5 and 10). Item 16 (measurement and sharing of outcomes) showed poor loading and limited alignment with the Italian context, where outcome monitoring and dissemination are not consistently embedded in organizational culture–especially in public services–and are often framed within clinical governance rather than nursing‐led EBP; this pattern is consistent with literature describing inconsistent, compliance‐driven outcome measurement and weaker EBP monitoring in resource‐constrained settings (Fiore et al. 2024 ), Items 17a–c relate to shared governance/point‐of‐care authority, reflecting organizational structure more than EBP‐specific readiness; core readiness is better captured by leadership commitment and resource allocation items. These removed elements may be assessed separately and reconsidered as systems evolve (Caci et al. 2025 ). The removal of items 17a–c from the scale warrants a careful discussion of psychometric and conceptual implications. Empirically, items 17a and 17c showed weak loadings, while item 17b formed a highly loading but under‐identified factor, reducing construct stability and interpretability. For these reasons, items 17a–c were excluded from the core factor model. Psychometrically, their removal was necessary to ensure that the scale maintained its internal consistency and reliability by focusing on core dimensions that most accurately reflect EBP readiness. Conceptually, while shared governance and point‐of‐care decision authority can facilitate EBP decision making, these items reflect organizational structure more than EBP‐specific culture/readiness. Other OCRSIEP items, such as leadership commitment and resource allocation, are more directly tied to the EBP implementation. The removal of items 17a–c sharpens the scale's focus on the critical organizational supports that foster EBP readiness, ensuring that it measures factors that directly influence the ability of healthcare institutions to implement evidence‐based practices effectively. This decision highlights the importance of a streamlined approach in scale construction. While inclusion of all potentially relevant aspects of EBP culture seems essential, an overly complex scale risks diminishing the precision and utility of the scale. By removing these items, the scale's psychometric properties have been enhanced, reducing potential sources of measurement error and ensuring the focus on core constructs. Future research may explore these removed items in alternative contexts or measure them separately to assess their relevance in broader models of healthcare organizational culture. The bifactor analysis supported a strong general factor, confirming the conceptual validity of a unified construct and justifying the use of a total score, while reliability indices also supported meaningful interpretation of subscale scores. Particular caution is warranted for the ‘Knowledge and Resources’ subscale (F3), which showed comparatively lower internal consistency and S/N ratio. This factor includes items related to access to digital resources and the role of librarians in supporting EBP; its weaker performance may reflect contextual variability, as librarians are not systematically embedded in EBP infrastructures in many Italian healthcare organizations, reducing response variance and internal coherence (Bianchi et al. 2018 ; Fernández‐Salazar et al. 2021 ). Using this model, nurses in public services showed significantly lower latent means across all six OCRSIEP dimensions versus private settings, particularly for leadership support, organizational communication, and perceived benefits of change. The non‐invariant items highlight specific gaps in public organizations (e.g., reduced digital access), consistent with literature describing structural and cultural constraints to EBP implementation in public systems (Cleary‐Holdforth et al. 2022 ; Yoo et al. 2019 ). Cross‐national comparisons further highlight how healthcare organizations with stronger EBP orientation tend to foster more supportive, resource‐rich, and change‐ready environments (Hu et al. 2025 ). The Italian adapted OCRSIEP offers a useful operational contribution and can be applied at team, unit, institutional, and system levels to assess readiness for EBP. It supports internal audits, benchmarking, and pre/post evaluation of training or implementation initiatives, and can identify priority gaps (e.g., leadership, communication, resources) to guide targeted action. Evidence from oncology and pediatric settings similarly shows that monitoring readiness is a key predictor of successful evidence‐based innovation implementation (Melnyk et al. 2014 ; Puchalski Ritchie and Straus 2019 ). At the research level, the tool can support longitudinal studies evaluating organizational impact of EBP interventions and can be used alongside measures of individual behaviors, attitudes, and competence. The scale may also be adapted to other European languages, enabling more robust cross‐national comparisons in implementation science. 4.1. Limitations Limitations include: (i) the cross‐sectional design, which precludes responsiveness testing and calls for longitudinal studies to assess stability and sensitivity to interventions; (ii) convenience online sampling, potentially over‐representing digitally connected or EBP‐engaged nurses and influencing responses to digital‐resource items; (iii) self‐reported data, which may introduce interpretation and response biases–future work should include cognitive interviewing and mixed‐methods approaches; and (iv) short‐form subgroup comparisons limited by MCMC non‐convergence in one tenure group, preventing some information‐criterion estimates and requiring cautious interpretation. Table 5 . Linking evidence to action. TABLE 5. Linking evidence to action. The Italian OCRSIEP scale is a valid and useful instrument to assess organization readiness for EBP. The short OCRSIEP scale is a valid instrument to use in high‐demand clinical settings where time constraints limit administration of longer measures. Use the Italian OCRSIEP scale to assess priority areas and set targeted actions. Track changes in OCRSIEP scale over time to evaluate the impact of interventions. Monitoring organizational EBP readiness is a key predictor of successful evidence‐based innovation implementation. Open in a new tab 5. Conclusions This study provides evidence supporting the validity and reliability of the Italian adapted OCRSIEP within the present sample. After the removal of five items, participant responses were consistent with a six‐domain structure and support the use of both domain‐specific and an overall score. Measurement invariance across key groups supports comparative use within this context. The Italian adapted OCRSIEP can inform strategic decisions by identifying readiness gaps, guiding implementation priorities, and monitoring change over time, while routine outcome indicators/dashboards should be used in parallel to evaluate the results of EBP implementation. Future studies should test predictive validity and responsiveness to change and explore cross‐cultural adaptation in other systems. Future research could explore the utility of the deleted items in measuring organizational culture readiness, assessing their relevance and potential integration into broader frameworks of EBP implementation. Funding The authors have nothing to report. Ethics Statement Ethical approval for the study was obtained from the local Ethics Committee Territoriale Lazio Area 2 (Protocol number 30.23 CET 2 CBM). The research adhered to recognized ethical standards and complied with the principles outlined in the Declaration of Helsinki [38]. Prior to survey participation, all individuals received detailed information regarding the study procedures and provided informed consent electronically. Participant data were stored securely, with access limited to authorized members of the research team, in accordance with confidentiality protocols and the requirements of the General Data Protection Regulation . Consent Informed consent was obtained from all subjects involved in the study. Conflicts of Interest The authors declare no conflicts of interest. Supporting information Table S1: Standardized Factor Loadings from the EFA ( n = 243). WVN-23-0-s001.docx (20.7KB, docx) Acknowledgments The authors sincerely appreciate the collaboration of all individuals who contributed to this research. Special thanks to those who played a role in sharing the questionnaire and to those who devoted their time to participate in and provide responses to the survey. Open access publishing facilitated by Azienda Socio Sanitaria Territoriale di Cremona, as part of the Wiley ‐ SBBL agreement. Data Availability Statement The data that support the findings of this study are available from the corresponding author upon reasonable request. References Beaton, D. E. , Bombardier C., Guillemin F., and Ferraz M. B.. 2000. “Guidelines for the Process of Cross‐Cultural Adaptation of Self‐Report Measures.” Spine 25, no. 24: 3186–3191. 10.1097/00007632-200012150-00014. [ DOI ] [ PubMed ] [ Google Scholar ] Bianchi, M. , Bagnasco A., Bressan V., et al. 2018. “A Review of the Role of Nurse Leadership in Promoting and Sustaining Evidence‐Based Practice.” Journal of Nursing Management 26, no. 8: 918–932. 10.1111/jonm.12638. [ DOI ] [ PubMed ] [ Google Scholar ] Bozzetti, M. , Cascio A. L., Napolitano D., et al. 2026. “Italian EBP Implementation Scales: A Psychometric Validation Study.” Worldviews on Evidence‐Based Nursing 23, no. 1: e70114. 10.1111/wvn.70114. [ DOI ] [ PubMed ] [ Google Scholar ] Caci, L. , Nyantakyi E., Blum K., et al. 2025. “Organizational Readiness for Change: A Systematic Review of the Healthcare Literature.” Implementation Research and Practice 6: 26334895251334536. 10.1177/26334895251334536. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Cevallos, M. , and Egger M.. 2014. “STROBE (STrengthening the Reporting of Observational Studies in Epidemiology).” In Guidelines for Reporting Health Research: A User's Manual, edited by Moher D., Altman D. G., Schulz K. F., Simera I., and Wager E. (A c. Di, 1a ed., 169–179. Wiley. 10.1002/9781118715598.ch17. [ DOI ] [ Google Scholar ] Cleary‐Holdforth, J. , Leufer T., Baghdadi N. A., and Almegewly W.. 2022. “Organizational Culture and Readiness for Evidence‐Based Practice in the Kingdom of Saudi Arabia: A Pre‐Experimental Study.” Journal of Nursing Management 30, no. 8: 4560–4568. 10.1111/jonm.13856. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Cronbach, L. J. , and Gleser G. C.. 1964. “The Signal/Noise Ratio in the Comparison of Reliability Coefficients.” Educational and Psychological Measurement 24, no. 3: 467–480. 10.1177/001316446402400303. [ DOI ] [ Google Scholar ] Fernández‐Salazar, S. , Ramos‐Morcillo A. J., Leal‐Costa C., García‐González J., Hernández‐Méndez S., and Ruzafa‐Martínez M.. 2021. “Evidence‐Based Practice Competency and Associated Factors Among Primary Care Nurses in Spain.” Atencion Primaria 53, no. 7: 102050. 10.1016/j.aprim.2021.102050. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Fiore, M. , Bianconi A., Acuti Martellucci C., et al. 2024. “Impact of the Italian Healthcare Outcomes Program (PNE) on the Care Quality of the Poorest Performing Hospitals.” Healthcare (Basel) 12, no. 4: 431. 10.3390/healthcare12040431. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Gelman, A. , Carlin J. B., Stern H. S., Dunson D. B., Vehtari A., and Rubin D. B.. 2013. Bayesian Data Analysis, Third Edition. CRC Press. [ Google Scholar ] Holtmann, J. , Koch T., Lochner K., and Eid M.. 2016. “A Comparison of ML, WLSMV, and Bayesian Methods for Multilevel Structural Equation Models in Small Samples: A Simulation Study.” Multivariate Behavioral Research 51, no. 5: 661–680. 10.1080/00273171.2016.1208074. [ DOI ] [ PubMed ] [ Google Scholar ] Hooge, N. , Allen D. H., McKenzie R., and Pandian V.. 2022. “Engaging Advanced Practice Nurses in Evidence‐Based Practice: An e‐Mentoring Program.” Worldviews on Evidence‐Based Nursing 19, no. 3: 235–244. 10.1111/wvn.12565. [ DOI ] [ PubMed ] [ Google Scholar ] Hu, L. , and Bentler P. M.. 1999. “Cutoff Criteria for Fit Indexes in Covariance Structure Analysis: Conventional Criteria Versus New Alternatives.” Structural Equation Modeling: A Multidisciplinary Journal 6, no. 1: 1–55. 10.1080/10705519909540118. [ DOI ] [ Google Scholar ] Hu, S. , Liu S., Li X., et al. 2025. “Organizational Evidence‐Based Practice Culture, Implementation Leadership, and Nurses: A Bidirectional Mediation Model.” International Nursing Review 72, no. 2: e13054. 10.1111/inr.13054. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Ivziku, D. , Giannetta N., D'Angelo D., et al. 2025. “Italian EBP Beliefs Scales: A Psychometric Validation Study.” Worldviews on Evidence‐Based Nursing 22, no. 3: e70049. 10.1111/wvn.70049. [ DOI ] [ PubMed ] [ Google Scholar ] Melnyk, B. M. , and Fineout‐Overholt E.. 2022. Evidence‐Based Practice in Nursing & Healthcare: A Guide to Best Practice. Lippincott Williams and Wilkins. [ Google Scholar ] Melnyk, B. M. , Fineout‐Overholt E., Giggleman M., and Choy K.. 2017. “A Test of the ARCC Model Improves Implementation of Evidence‐Based Practice, Healthcare Culture, and Patient Outcomes.” Worldviews on Evidence‐Based Nursing 14, no. 1: 5–9. 10.1111/wvn.12188. [ DOI ] [ PubMed ] [ Google Scholar ] Melnyk, B. M. , Fineout‐Overholt E., Giggleman M., and Cruz R.. 2010. “Correlates Among Cognitive Beliefs, EBP Implementation, Organizational Culture, Cohesion and Job Satisfaction in Evidence‐Based Practice Mentors From a Community Hospital System.” Nursing Outlook 58, no. 6: 301–308. 10.1016/j.outlook.2010.06.002. [ DOI ] [ PubMed ] [ Google Scholar ] Melnyk, B. M. , Fineout‐Overholt E., and Mays M. Z.. 2008. “The Evidence‐Based Practice Beliefs and Implementation Scales: Psychometric Properties of Two New Instruments.” Worldviews on Evidence‐Based Nursing 5, no. 4: 208–216. 10.1111/j.1741-6787.2008.00126.x. [ DOI ] [ PubMed ] [ Google Scholar ] Melnyk, B. M. , Gallagher‐Ford L., Long L. E., and Fineout‐Overholt E.. 2014. “The Establishment of Evidence‐Based Practice Competencies for Practicing Registered Nurses and Advanced Practice Nurses in Real‐World Clinical Settings: Proficiencies to Improve Healthcare Quality, Reliability, Patient Outcomes, and Costs.” Worldviews on Evidence‐Based Nursing 11, no. 1: 5–15. 10.1111/wvn.12021. [ DOI ] [ PubMed ] [ Google Scholar ] Melnyk, B. M. , Gallagher‐Ford L., Thomas B. K., Troseth M., Wyngarden K., and Szalacha L.. 2016. “A Study of Chief Nurse Executives Indicates Low Prioritization of Evidence‐Based Practice and Shortcomings in Hospital Performance Metrics Across the United States.” Worldviews on Evidence‐Based Nursing 13, no. 1: 6–14. 10.1111/wvn.12133. [ DOI ] [ PubMed ] [ Google Scholar ] Melnyk, B. M. , Hsieh A. P., Gallagher‐Ford L., et al. 2021. “Psychometric Properties of the Short Versions of the EBP Beliefs Scale, the EBP Implementation Scale, and the EBP Organizational Culture and Readiness Scale.” Worldviews on Evidence‐Based Nursing 18, no. 4: 243–250. 10.1111/wvn.12525. [ DOI ] [ PubMed ] [ Google Scholar ] Melnyk, B. M. , Hsieh A. P., and Mu J.. 2022. “Psychometric Properties of the Organizational Culture and Readiness Scale for System‐Wide Integration of Evidence‐Based Practice.” Worldviews on Evidence‐Based Nursing 19, no. 5: 380–387. 10.1111/wvn.12603. [ DOI ] [ PubMed ] [ Google Scholar ] Melnyk, B. M. , Zellefrow C., Tan A., and Hsieh A. P.. 2020. “Differences Between Magnet and Non‐Magnet‐Designated Hospitals in Nurses' Evidence‐Based Practice Knowledge, Competencies, Mentoring, and Culture.” Worldviews on Evidence‐Based Nursing 17, no. 5: 337–347. 10.1111/wvn.12467. [ DOI ] [ PubMed ] [ Google Scholar ] Miake‐Lye, I. M. , Delevan D. M., Ganz D. A., Mittman B. S., and Finley E. P.. 2020. “Unpacking Organizational Readiness for Change: An Updated Systematic Review and Content Analysis of Assessments.” BMC Health Services Research 20, no. 1: 106. 10.1186/s12913-020-4926-z. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Pendoni, R. , Motta P. C., Bozzetti M., and Marcomini I.. 2024. “Effectiveness of EBPEPU (Evidence‐Based Practice Educational Program in Undergraduate Nursing Education): A Before‐After Study.” Teaching and Learning in Nursing 19, no. 3: 225–228. 10.1016/j.teln.2024.02.017. [ DOI ] [ Google Scholar ] Polit, D. F. , Beck C. T., and Owen S. V.. 2007. “Is the CVI an Acceptable Indicator of Content Validity? Appraisal and Recommendations.” Research in Nursing and Health 30, no. 4: 459–467. 10.1002/nur.20199. [ DOI ] [ PubMed ] [ Google Scholar ] Puchalski Ritchie, L. M. , and Straus S. E.. 2019. “Assessing Organizational Readiness for Change Comment on «Development and Content Validation of a Transcultural Instrument to Assess Organizational Readiness for Knowledge Translation in Healthcare Organizations: The OR4KT».” International Journal of Health Policy and Management 8, no. 1: 55–57. 10.15171/ijhpm.2018.101. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Saunders, H. , Gallagher‐Ford L., Kvist T., and Vehviläinen‐Julkunen K.. 2019. “Practicing Healthcare Professionals' Evidence‐Based Practice Competencies: An Overview of Systematic Reviews.” Worldviews on Evidence‐Based Nursing 16, no. 3: 176–185. 10.1111/wvn.12363. [ DOI ] [ PubMed ] [ Google Scholar ] Shea, C. M. , Jacobs S. R., Esserman D. A., Bruce K., and Weiner B. J.. 2014. “Organizational Readiness for Implementing Change: A Psychometric Assessment of a New Measure.” Implementation Science 9: 7. 10.1186/1748-5908-9-7. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Steen, J. , and Stewart C.. 2024. “A Psychometric Analysis of the Organizational Readiness for Implementing Change (ORIC) in a Child Welfare Setting.” Journal of Evidence‐Based Social Work (2019) 21, no. 6: 704–719. 10.1080/26408066.2024.2409092. [ DOI ] [ PubMed ] [ Google Scholar ] Taber, K. S.
2018. “The Use of Cronbach's Alpha When Developing and Reporting Research Instruments in Science Education.” Research in Science Education 48, no. 6: 1273–1296. 10.1007/s11165-016-9602-2. [ DOI ] [ Google Scholar ] Vehtari, A. , Gelman A., and Gabry J.. 2017. “Practical Bayesian Model Evaluation Using Leave‐One‐Out Cross‐Validation and WAIC.” Statistics and Computing 27, no. 5: 1413–1432. 10.1007/s11222-016-9696-4. [ DOI ] [ Google Scholar ] Wudu, M. A. , Tarekegn S. M., Wondifraw E. B., et al. 2024. “Uptake of Evidence‐Based Practice and Its Predictors Among Nurses in Ethiopia: A Systematic Review and Meta‐Analysis.” Frontiers in Pharmacology 15: 1421690. 10.3389/fphar.2024.1421690. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Yoo, J. Y. , Kim J. H., Kim J. S., Kim H. L., and Ki J. S.. 2019. “Clinical Nurses' Beliefs, Knowledge, Organizational Readiness and Level of Implementation of Evidence‐Based Practice: The First Step to Creating an Evidence‐Based Practice Culture.” PLoS One 14, no. 12: e0226742. 10.1371/journal.pone.0226742. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Supplementary Materials Table S1: Standardized Factor Loadings from the EFA ( n = 243). WVN-23-0-s001.docx (20.7KB, docx) Data Availability Statement The data that support the findings of this study are available from the corresponding author upon reasonable request. Articles from Worldviews on Evidence-Based Nursing are provided here courtesy of Wiley ACTIONS View on publisher site PDF (644.3 KB) Cite Collections Permalink PERMALINK Copy RESOURCES Similar articles Cited by other articles Links to NCBI Databases Cite Copy Download .nbib .nbib Format: AMA APA MLA NLM Add to Collections Create a new collection Add to an existing collection Name your collection * Choose a collection Unable to load your collection due to an error Please try again Add Cancel Follow NCBI NCBI on X (formerly known as Twitter) NCBI on Facebook NCBI on LinkedIn NCBI on GitHub NCBI RSS feed Connect with NLM NLM on X (formerly known as Twitter) NLM on Facebook NLM on YouTube National Library of Medicine 8600 Rockville Pike Bethesda, MD 20894 Web Policies FOIA HHS Vulnerability Disclosure Help Accessibility Careers NLM NIH HHS USA.gov Back to Top