UCEEM

Summary: Introduction: Assessment of the learning environment is essential for theconstant improvement of the quality of teaching and, consequently, the achievement oflearning objectives by students. Taking into account the absence of instruments inPortuguese of Portugal to evaluate the learning environment in a clinical environment,this work aimed to validate the Undergraduate Clinical Education Environment Measureinstrument for Portuguese of Portugal, through the cross-cultural adaptation of thisinstrument. Materials and methods: The target population of the project were medicalstudents from the University of Beira Interior who attended curricular clinical internshipsin the 2022/2023 academic year. A focus group was held to formulate a first versionadapted into Portuguese, a version that was reviewed by experts in the field. This versionwas then applied in a pre-test, followed by individual interviews. The results obtainedwere discussed, the instrument was revised and the distribution of responses wasanalyzed. Finally, the final version of the adapted instrument was applied to the targetpopulation, with statistical treatment and evaluation of the instrument's psychometricproperties subsequently carried out. Results : The first adapted version was applied in apre-test to 34 participants, generating a final version to be applied to the targetpopulation. 270 valid responses were obtained, with confirmatory factor analysisdemonstrating that the instrument adapted for the Portuguese context is valid andreliable, with an Omega coefficient of 0.96. Conclusion : Further reflection on the question“I have adequate access to computers” will be necessary given the national panorama onmedical students’ access to clinical records. However, the instrument adapted for thePortuguese context proved to be valid and reliable, being ready to be used to evaluate thelearning environment in a clinical environment for medical students.

Introduction
The quality of clinical learning environments (CLEs) impact learners' experience and competence development. Objective instruments have been developed to evaluate CLEs from the perspective of medical and nursing students. No such instruments have been used in Radiography.

Methods
Radiography students' perspectives of CLEs were investigated using the validated Undergraduate Clinical Education Environment Measure (UCEEM). Undergraduate (UG) (n = 365) and graduate entry (GE) radiography students (n = 45) from an Irish university were invited to participate. CLEs consisted of 25 public and 10 private hospitals. The UCEEM contains 25 items under two overarching dimensions, experiential learning and social participation, with four subscales: opportunities to learn in and through work and quality of supervision; preparedness for student entry; workplace interaction patterns and student inclusion; and equal treatment.

Results
Two hundred and fifteen students participated (response rate 52.4%; n = 185 UG, n = 30 GE), most of whom were based in public hospitals (87.4%). The mean UCEEM score was 107.5 ± 17.7 (optimal range: 90–125). Experiential learning and social participation were scored 74.2 ± 12.5 (optimal range: 60–85) and 33.37 ± 6.29 (optimal range: 30–40), respectively. Private hospitals ranked slightly higher than public hospitals for ‘opportunities to learn in and through work and quality of supervision’ (p = 0.018). Items ranked highest related to ‘equal treatment’ and ‘opportunity to put theory into practice’. Clinical supervision-related items were scored highest by first-year students.

Conclusion
High UCEEM scores indicate radiography students' positive perceptions of their CLEs, particularly regarding equality and experiential learning. Findings support the expansion of radiography placement to include public and private hospitals.

Implications for practice
The UCEEM is a theoretically robust, validated tool which appears suitable for evaluating radiography CLEs. This study provides valuable baseline data for comparison of Radiography CLEs.

Recognizing the factors affecting clinical competence among internship nursing students is crucial. This study was aimed at determining the effects of self-directed learning on internship nursing students’ clinical competence under the mediating role of the clinical learning environment.

Methods
This cross-sectional research was performed on 300 internship nursing students selected by convenience sampling with a structural equation modeling (SEM) approach. Data were collected in one stage with three tools: the Self-Directed Learning Readiness Scale for Nursing Education, Education Environment Measure, and Clinical Competence Questionnaire. Data analysis was performed in SPSS version 21 and Smart-PLS version 3 with partial least squares-SEM. Measurement and structural model data were assessed with a significance threshold of p < 0.05.

Results
A total of 20.5 % of the variance in clinical competence was explained by self-directed and clinical learning environments. Self-directed learning had a significant positive effect on clinical competence (path coefficient = 0.14, 95 % CI: 0.02, 0.26; p = 0.027), and on the clinical learning environment (path coefficient = 0.41, 95 % CI: 0.31, 0.52; p < 0.001). A relationship was observed between the clinical learning environment and clinical competence (path coefficient = 0.38, 95 % CI: 0.25, 0.50; p < 0.001). The indirect effect of self-directed learning on clinical competence was positive and significant (path coefficient = 0.11, 95 % CI: 0.07, 0.17; p < 0.001). Self-directed learning had a significant total effect on clinical competence (path coefficient = 0.30, 95 % CI: 0.19, 0.40; p < 0.001).

Conclusions
According to the results, we recommend that nursing education managers and instructors consider plans to enhance self-directed learning among nursing students and improve the clinical learning environment.

ntroduction: It is crucial to recognize the factors affecting the clinical competence of internship nursing students. This study aimed to determine the effects of self-directed learning on the clinical competence of internship nursing students with the mediating role of the clinical learning environment. Methods: This cross-sectional research was performed on 300 internship nursing students selected by convenience sampling. Data were collected in one stage using three tools of Self-Directed Learning Readiness Scale for Nursing Education, Education Environment Measure, and Clinical Competence Questionnaire. Data analysis was performed in SPSS version 21 and Smart-PLS version 3 using Partial least squares-SEM with considering a p-value of 0.05. Results: The results showed that 20.5% of the variance in clinical competence could be explained by selfdirected and clinical learning environments. Self-directed learning had a positive and signi cant effect on clinical competence (path coe cient=0.14, 95% CI: 0.02, 0.26; p=0.027), and on clinical learning environment as well (path coe cient=0.41, 95% CI: 0.31, 0.52; p<0.001). A relationship was reported between clinical learning environment and clinical competence (path coe cient=0.38, 95% CI: 0.25, 0.50; p<0.001). The indirect effect of self-directed learning on clinical competence was positive and signi cant (path coe cient=0.11, 95% CI: 0.07, 0.17; p<0.001). Self-directed learning has a signi cant total effect on clinical competence (path coe cient=0.30, 95% CI: 0.19, 0.40; p<0.001). Conclusions: It is recommended that nursing education managers and instructors consider some plans to enhance self-directed learning among nursing students and improve the clinical learning environment.

Background
The clinical placements of our medical students are almost equally distributed across private and public sectors. This study aims to assess medical students’ perceptions of their Clinical learning Environment (CLE) across these two different healthcare settings, using the Undergraduate Clinical Education Environment Measure (UCEEM).

Methods
76 undergraduate medical students (Year 5 and 6), were invited to participate. Data were collected using an online UCEEM with additional questions related to demographics and case load exposure. The UCEEM consists of two overarching domains of experiential learning and social participation, with four subdomains of learning opportunities, preparedness, workplace interaction, and inclusion.

Results
38 questionnaires were received. Of 225 responses to the individual UCEEM items, 51 (22.6%) scored a mean of ≥ 4 (range 4-4.5, representing strong areas), 31 (13.7%) scored a mean of ≤ 3 (range 2.1-3, needing attention) and 143 (63.6%) scored a mean of 3.1–3.9 (areas that could be improved). The majority (63%) of the case load exposure responses scored a mean of ≥ 4 (range 4-4.5). Compared to the private sittings, there is a significant reduction in total UCEEM (p = 0.008), preparedness for student entry (p = 0.003), and overarching dimension of social participation (p = 0.000) scores for the public sector. Similarly, both workplace interaction patterns and student inclusion and equal treatment scored significantly lower for the public sector (p = 0.000 and p = 0.011 respectively). Two out of three case load exposure items scored significantly higher for the public sector (p = 0.000).

Discussion
The students’ CLE perceptions were generally positive. The lower UCEEM ratings in the public sector items were related to student entry preparedness, workplace interactions, student inclusiveness and workforce equity of treatment. In contrast the students were exposed to more variety and larger number of patients in the public sector. These differences indicated some significantly different learning environments between the two sectors.

Learning environments shape the experiences of learners and practitioners, making them an important component of program evaluation. However, educators find it challenging to decide whether to measure clinical learning environments with existing instruments or to design their own new instrument and, if using an existing instrument, which to choose. To assist educators with these decisions, the authors compared clinical learning environment instruments based on their characteristics, underlying constructs, and degree to which items reflect 4 domains (personal, social, organizational, material) from a recently developed model for conceptualizing learning environments in the health professions. Building on 3 prior literature reviews as well as a literature search, the authors identified 6 clinically oriented learning environment instruments designed for medical education. They collected key information about each instrument (e.g., number of items and subscales, conceptual frameworks, operational definitions of the learning environment) and coded items from each instrument according to the 4 domains. The 6 instruments varied in number of items, underlying constructs, subscales, definitions of clinical learning environment, and domain coverage. Most instruments focused heavily on the organizational and social domains and less on the personal and material domains (half omitted the material domain entirely). The variations in these instruments suggest that educators might consider several guiding questions. How will they define the learning environment and which theoretical lens is most applicable (e.g., personal vitality, sociocultural learning theory)? What aspects or domains of the learning environment do they most wish to capture (e.g., personal support, social interactions, organizational culture, access to resources)? How comprehensive do they want the instrument to be (and correspondingly how much time do they expect people to devote to completing the instrument and how frequently)? Whose perspective do they wish to evaluate (e.g., student, resident, fellow, attending, team, patient)? Each of these considerations is addressed.

Objective
Patient-centred learning (PCL) allows medical students to practice a patient-centred approach; however, didactic characteristics of PCL have yet to be fully elucidated. Clinical placements structured as a student clinic (SC) enable authentic student-patient learning relations through enhanced student responsibility and can serve as examples of PCL. We explored the didactic characteristics of supervision and learning in SCs to provide recommendations for PCL-oriented medical education.

Methods
Triangulation mixed methods study based on qualitative data collected from in-depth interviews with clinical teachers and quantitative data collected from student evaluations of supervision and learning in the SCs.

Results
Supervision and learning in SCs were characterized by 1) a focus on student-patient compatibility and patient needs and resources, which indicated PCL, 2) person-centred explorative supervision to adjust challenges to students’ needs and resources, and 3) support of student autonomy to take responsibility for patient treatment.

Conclusion
PCL was facilitated by clinical teachers through a dual person-centred didactic approach combined with autonomy-supportive didactic practice. This enabled the integration of patients’ and students’ needs and resources in clinical teaching.

Objective: To explore how students in the pre-clerkship phase perceive the workplace clinical learning environment based on their experiential learning and social participation.

Method: The cross-sectional study was conducted at the Gulf Medical University, United Arab Emirates in June 2019 and comprised 2nd and 3rd year medical students. Data was collected using the undergraduate clinical education environment measure tool which is a valid and reliable instrument to measure clinical learning environment for undergraduate medical students.

Results: There were 858 students, and 70.3% of the students expressed satisfaction with clinical learning environment categories.

Conclusion: The average satisfaction level of the students regarding the quality of supervision was better than that of workplace environment and learning opportunities provided.

Objectives
Improving medical student placements relies on being able to reliably evaluate how students experience clinical learning environments. The Undergraduate Clinical Education Environment Measure (UCEEM) is an increasingly used validated tool designed to allow such evaluations. This study aims to further characterize how the UCEEM relates to qualitative evaluation.
methods
Students on placement at one hospital were invited to complete the UCEEM before and after the implementation of an innovative new placement structure. Additionally, focus groups were employed to collect qualitative data on their experiences. a novel protocol to triangulate the output of the UCEEM with the qualitative data was developed.
Results
The UCEEM showed good internal consistency (Cronbach's Alpha 0.79-0.91) and internal correlation. Implementation of the intervention created significant improvements in the overall UCEEM scores (P = .008) and in the “Learning in and through work and quality of supervision” (P = .048), “Preparedness for student entry” (P = .033) and “Workplace interaction patterns and student inclusion” (P = .039) domains. The triangulation of qualitative data with UCEEM output showed that the UCEEM allowed evaluation of some perceptions not reached through open questioning. However, mixed interpretations of UCEEM items by students led to the conflation of themes and challenges in deriving the meaning behind the score. This appeared to be the case for 14 of the 24 UCEEM items.
Conclusion
This investigation adds to the literature supporting the UCEEM as a validated tool. It also elucidates the limitations and relationships to qualitative data that investigators need to be aware of in its use.

Objective To develop a clinical learning environment scale for Chinese nursing students and test its reliability and validity. Methods Based on Moos social environment theory, qualitative interviews and expert consultations were used to develop the pre-test version of the Chinese Nursing Students' Clinical Learning Environment Scale. With a convenience sampling method, 255 and 1582 Chinese nursing students were selected as the prediction and validation samples from December 2022 to March 2023. Exploratory and con rmatory factor analyses were conducted to determine the nal version of the scale and test its reliability and validity. Results The scale consisted of 19 items. Exploratory factor analysis extracted three characteristic roots in terms of goal oriented, interpersonal relation, and learning support, which explained 71.500% of the total variance. The results of the con rmatory factor analysis showed that the GFI was 0.848, AGFI was 0.806, RMSEA was 0.090, RMR was 0.041, NFI was 0.910, IFI was 0.916, CFI was 0.916, PCFI was 0.798, and PNFI was 0.793. The reliability values of the three dimensions were 0.870, 0.858, and 0.943, respectively, and the convergent validity values were 0.574, 0.603, and 0.625, respectively. Conclusion The reliability and validity of the dimensions of the Chinese Nursing Students' Clinical Learning Environment Scale are acceptable, which can be used as a useful tool to measure the clinical learning environment of Chinese nursing students.

Purpose
The purpose of this study was to identify the possible correlations of ‘satisfaction with clinical practice (SA)’ with ‘clinical learning environment (EN)’ and ‘clinical practice stress (ST).’ We searched for the mediating effect of ‘clinical practice stress’ on ‘satisfaction with clinical practice’ when the clinical learning environment influences ‘satisfaction with clinical practice.’
Methods
This research investigated 208 medical and nursing students attending the school of medicine and nursing in Korea. The total number of nursing students was 135 (64.9%); 73 medical students participated (35.1%). We used the Korean-Undergraduate Clinical Education Environment in 24 questions for EN, ST scale in 24 questions, and SA scale in 10 questions. We performed measurement structural equation model analysis to identify a path of the model.
Results
Medical students had significantly higher levels of ST. EN had a significant negative correlation with ST and a significant positive correlation with SA. The ST had a significant negative correlation with SA. The results of the goodness of fit index have fulfilled the criteria of goodness of fit. There was a significant mediating effect of ST on SA when EN influences SA.
Conclusion
The clinical learning environment affected satisfaction with the clinical practice directly or indirectly mediated by clinical practice stress. Therefore, educational institutes should try to increase satisfaction with clinical practice by continuously monitoring and improving the clinical learning environment in addition to taking measures for decreasing the clinical practice stress.

ntroduction/aim: The measurement of the learning environment in medicine is fundamental to assess the quality of medical education. Multiple instruments are available for this purpose, but their content, theoretical foundations and practical implications variate. Our aim is to synthesize the content validity and scope of these instruments.

Methods: We conducted a systematic review of original studies designed to measure the learning environment in medicine, published in English and Spanish languages, from 1990 to 2018. We search for studies in Pubmed, ERIC, British Nursing Index, Google Scholar, Science Direct, Cochrane, CINALH and Latindex. We analyzed the main domains, theoretical foundations and methods to determine the content validity of each instrument.

Results: Thirty-eight studies evaluating twelve instruments (one available in Spanish) were included. Two instruments were specific for the surgical environment and one for the community. Only three instruments were supported by an educational theory. The domains most frequently evaluated were the atmosphere, social perception and academic perception of learning. The content validity was established through expert panels or Delphi method in 83,3 % of the instruments.

Conclusions: There are several instruments to evaluate a complex construct such as the learning environment in medicine. In general, these instruments have content validity supported by multiple sources and methodologies and they can be used by medical educators to assess the learning environment comprehensively.

Palabras clave: educación médica, estudiantes de medicina, ambientes de aprendizaje, validez de contenido, medical education, undergraduates, learning environments, content validity.

Introduction: The educational environment for the clinical teaching of undergraduate medical and nursing students influences the students’ perception of significant aspects of satisfaction in the clinical learning environment. Objective: To transculturally adapt, with the concept, content and construct validity, the instrument “Undergraduate Clinical Education Environment Measure – UCEEM” into the Brazilian Portuguese language and validate the translated and adapted version; involving medical and nursing students, while experiencing clinical teaching during their undergraduate course. Method: This is a methodology study aimed at the translation, adaptation and transcultural validation process, which is divided into six steps, as proposed by Beaton. The steps consisted in the translation, translation synthesis, back-translation, proofreading by the experts committee, pre-testing and submission of the final version of the instrument to the author. Five bilingual translators participated in this process (three for the translation and two for the back-translation), as well as eight expert professionals who work as academic faculty and have Master’s and Doctoral degrees. The validation was carried out at two moments: the first one by using semantic, idiomatic, cultural and conceptual equivalence (pre-test), involving 30 medical and nursing students going through clinical teaching; and the second moment, the validation and assessment of the psychometric properties with 161 students from these same courses. Result: The transcultural adaptation carried out by eight expert professionals in this area was performed based on the experts’ considerations, in addition to the analysis and considerations of the researcher and author of the original instrument. The result of this phase led to the final version of this instrument in Brazilian Portuguese, which shows 26 items divided into two dimensions and four subscales that were maintained according to its original form. Following the filling out of the instrument by the nursing and medical students, the analysis of the Theory of Response revealed to be reliable, as determined by a Cronbach’s alpha of 0.917. According to Spearman’s correlation test, the results indicate a positive correlation between the questions. Conclusions: The Portuguese version of the UCEEM instrument showed to be adequate and useful to evaluate the satisfaction with the educational environment in clinical teaching according to the perception of medical and nursing students.

Objective: To determine the viewpoints of nursing and midwifery students regarding the
clinical learning environment and to identify the relationship between these viewpoints with
some demographic characteristics of students.
Methods: This cross-sectional study was carried out on nursing and midwifery students
(n=119) in the educational hospitals affiliated to Kermanshah University of Medical Sciences
from February to June 2017. The data-gathering tools were a demographics questionnaire
and the standard undergraduate clinical education environment measure. This tool comprises
two main scales of learning through experiencing and social participation, with four subscales of opportunities to learn in and through work and quality of supervision, preparedness
for student entry, workplace interaction patterns and student inclusion, and equal treatment.
The score ranged from 25 to 125 and the higher the score the higher the quality of the
educational environment. The collected data were analyzed in “Statistical Package for the Social Sciences” (Version20).
Results: The nursing and midwifery students’ viewpoint of the clinical learning environment was suboptimal. There was a significant difference between the female and male students’ viewpoints of the clinical learning environment (p=0.007). There was no significant difference between students’ viewpoints of the clinical learning environment with marital status, employment, domicile, grade-point average, and number of days of internship.
Nursing students had more positive viewpoints of the clinical learning environment than midwifery students (p=0.001). Conclusion: The results showed that the clinical learning environment was suboptimal based on the nursing and midwifery students’ perspectives. Intervention to improve the clinical learning environment is recommended

Background: Students" perceptions of the educational environment are an important construct in assessing and enhancing the quality of medical training programs. Reliable and valid measurement, however, can be problematic especially as instruments developed and tested in one culture are translated for use in another. Materials and method: This study sought to explore the psychometric properties of the undergraduate Clinical Educational Environment Measure (UCEEM) for use in Iranian nursing and midwifery students. We translated the instrument into Persian and ensured its content validity by back translation and expert review prior to administering it to 215 nursing and midwifery Students Tehran Medical Sciences branch Islamic Azad University. Results: One hundred and ninety seven questionnaires were analyzed. The factor analysis yielded four factors: F1: Opportunists to learn in and through work and Quality of supervision (8 items), F2Preparedness for student entry (8 items), and F3: Workplace interaction patterns and student inclusion Supervision (7 items), F4: Equal treatment (2) items. All correlations were r>0.3. Pearson"s correlation coefficients indicated that the relationships between subscales conformed to the theoretical model. Coefficients for subscales ranged between 0.27 and 0.75. All correlations were significant (p<0.005). The equal treatment subscale had a lower correlation with the other subscales (range 0.27-0.36).Relationships between the other three subscales varied between 0.53 and 0.75 and the reliabilities (Cranach"s α) of the items was 0.93. Conclusion: The Persian version of the UCEEM appears to be a reliable and potentially valid instrument for use in Iranian nursing and midwifery schools and may find favor in evaluating the educational environments of nursing and midwifery programs nationwid