Luca Grassetti

Instruments evaluating the quality of the clinical learning environment in nursing education: A systematic review of psychometric properties

BACKGROUND The clinical learning environment is fundamental to nursing education paths, capable of affecting learning processes and outcomes. Several instruments have been developed in nursing education, aimed at evaluating the quality of the clinical learning environments; however, no systematic review of the psychometric properties and methodological quality of these studies has been performed to date. OBJECTIVES The aims of the study were: 1) to identify validated instruments evaluating the clinical learning environments in nursing education; 2) to evaluate critically the methodological quality of the psychometric property estimation used; and 3) to compare psychometric properties across the instruments available. DESIGN A systematic review of the literature (using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines) and an evaluation of the methodological quality of psychometric properties (using the COnsensus-based Standards for the selection of health Measurement INstruments guidelines). DATA SOURCES The Medline and CINAHL databases were searched. Eligible studies were those that satisfied the following criteria: a) validation studies of instruments evaluating the quality of clinical learning environments; b) in nursing education; c) published in English or Italian; d) before April 2016. REVIEW METHODS The included studies were evaluated for the methodological quality of the psychometric properties measured and then compared in terms of both the psychometric properties and the methodological quality of the processes used. RESULTS The search strategy yielded a total of 26 studies and eight clinical learning environment evaluation instruments. A variety of psychometric properties have been estimated for each instrument, with differing qualities in the methodology used. Concept and construct validity were poorly assessed in terms of their significance and rarely judged by the target population (nursing students). Some properties were rarely considered (e.g., reliability, measurement error, criterion validity), whereas others were frequently estimated, but using different coefficients and statistical analyses (e.g., internal consistency, structural validity), thus rendering comparison across instruments difficult. Moreover, the methodological quality adopted in the property assessments was poor or fair in most studies, compromising the goodness of the psychometric values estimated. CONCLUSIONS Clinical learning placements represent the key strategies in educating the future nursing workforce: instruments evaluating the quality of the settings, as well as their capacity to promote significant learning, are strongly recommended. Studies estimating psychometric properties, using an increased quality of research methodologies are needed in order to support nursing educators in the process of clinical placements accreditation and quality improvement.

A note on transformed likelihood approach in linear dynamic panel models

The transformed likelihood approach to estimation of fixed effects dynamic panel data models is shown to present very good inferential properties but it is not directly implemented in the most diffused statistical software. The present paper aims at showing how a simple model reformulation can be adopted to describe the problem in terms of classical linear mixed models. The transformed likelihood approach is based on the first differences data transformation, the following results derive from a convenient reformulation in terms of deviations from the first observations. Given the invariance to data transformation, the likelihood functions defined in the two cases coincide. Resulting in a classical random effect linear model form, the proposed approach significantly improves the number of available estimation procedures and provides a straightforward interpretation for the parameters. Moreover, the proposed model specification allows to consider all the estimation improvements typical of the random effects model literature. Simulation studies are conducted in order to study the robustness of the estimation method to mean stationarity violation.

[Validation of the Italian Clinical Learning Environment Instrument (SVIAT): study protocol].

UNLABELLED . Validation of the Italian Clinical Learning Environment Instrument (SVIAT): study protocol. INTRODUCTION Nursing students obtain most of their university credits in internship environments whose quality can affect their clinical learning. Several tools are available to measure the quality of the clinical learning environment (CLE) as perceived by students: these instruments developed in other countries, were validated in Italian but do not discriminate those CLEs capable (or not) to promote significant clinical learning. AIM To validate an instrument to measure the capability of the CLE to generate clinical learning; the secondary aim is to describe the learning environments as perceived by nursing students according to individual course site and tutorial models adopted. METHODS The study will be developed in three phases: a) instrument development and pilot phase, b) validation of the psychometric properties of the instrument and c) description of the CLEs as perceived by the students including factors/item confirmed in the validation process. Expected outcomes. A large validation, with more than 8,000 participating students is expected; the construct under lying will be confirmed through exploratory and confirmatory factor analysis and will report high internal consistency; the instrument will report also a high test-retest and inter-rater reliability; in addition, the instrument will demonstrate predictive ability by discriminating those units able (or not) to activate effective learning processes.

Semiparametric stochastic frontier models for clustered data

The mixed model approach to semiparametric regression is considered for stochastic frontier models, with focus on clustered data. Standard assumptions about the model component representing the inefficiency effect lead to a closed skew normal distribution for the response. Model parameters are estimated by a generalization of restricted maximum likelihood, and random effects are estimated by an orthodox best linear unbiased prediction procedure. The method is assessed by means of Monte Carlo studies, and illustrated by an empirical application on hospital productivity.

Opportunity to discuss ethical issues during clinical learning experience

Background: Undergraduate nursing students have been documented to experience ethical distress during their clinical training and felt poorly supported in discussing the ethical issues they encountered. Research aims: This study was aimed at exploring nursing students’ perceived opportunity to discuss ethical issues that emerged during their clinical learning experience and associated factors. Research design: An Italian national cross-sectional study design was performed in 2015–2016. Participants were invited to answer a questionnaire composed of four sections regarding: (1) socio-demographic data, (2) previous clinical learning experiences, (3) current clinical learning experience quality and outcomes, and (4) the opportunity to discuss ethical issues with nurses in the last clinical learning experience (from 0 – ‘never’ to 3 – ‘very much’). Participants and research context: Participants were 9607 undergraduate nursing students who were attending 95 different three-year Italian baccalaureate nursing programmes, located at 27 universities in 15 Italian regions. Ethical considerations: This study was conducted in accordance with the Human Subject Research Ethics Committee guidelines after the research protocol was approved by an ethics committee. Findings: Overall, 4707 (49%) perceived to have discussed ethical issues ‘much’ or ‘very much’; among the remaining, 3683 (38.3%) and 1217 (12.7%) students reported the perception of having discussed, respectively, ‘enough’ or ‘never’ ethical issues emerged in the clinical practice. At the multivariate logistic regression analysis explaining 38.1% of the overall variance, the factors promoting ethical discussion were mainly set at the clinical learning environment levels (i.e. increased learning opportunities, self-directed learning, safety and nursing care quality, quality of the tutorial strategies, competences learned and supervision by a clinical nurse). In contrast, being male was associated with a perception of less opportunity to discuss ethical issues. Conclusion: Nursing faculties should assess the clinical environment prerequisites of the settings as a context of student experience before deciding on their accreditation. Moreover, the nursing faculty and nurse managers should also enhance competence with regard to discussing ethical issues with students among clinical nurses by identifying factors that hinder this learning opportunity in daily practice.

E-waste collection in Italy: Results from an exploratory analysis

This study looks at the performance of household electrical and electronic waste (WEEE) collection in 20 Italian regions from 2008 to 2015. The impact of several explicative variables on the results of e-waste collection is evaluated. The independent variables are socio-economic and demographic ones (age, gender, household size, education level, migration and income) along with technical-organisational variables (population density, presence of metropoles, macro regions, characteristics of the territory, percentage of household waste collected separately and number of e-waste collection points). The results show that the presence of collection points, the percentage of household waste collected separately and the percentage of females are positively correlated with the kg collected per inhabitant per year. For example, a variation of 1% of input (presence of collection points) corresponds to a 0.25% variation in the output (collection results) while 1% difference in the percentage of females in the population corresponds to a 7.549% difference in the collection rate. Population density, instead, is negatively correlated. It is interesting to note that there is a discrepancy between the Southern regions and the Centre regions (the former have an outcome 0.66 times lower than the latter) while the Northern regions perform similarly to the Centre ones. Moreover, the first year (2008) had a very low performance compared to the following years when the scheme constantly improved, mainly due to the additional collection points available. The Stochastic Frontier Model allows for the identification of the optimal production function among the 20 Italian regions. The best performing region is Tuscany (in the Centre), followed by Sardinia and Sicily (in the South).

Overcoming redundancies in bedside nursing assessments by validating a parsimonious meta-tool: findings from a methodological exercise study.

Rationale There is growing interest in validating tools aimed at supporting the clinical decision-making process and research. However, an increased bureaucratization of clinical practice and redundancies in the measures collected have been reported by clinicians. Redundancies in clinical assessments affect negatively both patients and nurses. Methods To validate a meta-tool measuring the risks/problems currently estimated by multiple tools used in daily practice. A secondary analysis of a database was performed, using a cross-validation and a longitudinal study designs. In total, 1464 patients admitted to 12 medical units in 2012 were assessed at admission with the Brass, Barthel, Conley and Braden tools. Pertinent outcomes such as the occurrence of post-discharge need for resources and functional decline at discharge, as well as falls and pressure sores, were measured. Explorative factor analysis of each tool, inter-tool correlations and a conceptual evaluation of the redundant/similar items across tools were performed. Therefore, the validation of the meta-tool was performed through explorative factor analysis, confirmatory factor analysis and the structural equation model to establish the ability of the meta-tool to predict the outcomes estimated by the original tools. Results High correlations between the tools have emerged (from r 0.428 to 0.867) with a common variance from 18.3% to 75.1%. Through a conceptual evaluation and explorative factor analysis, the items were reduced from 42 to 20, and the three factors that emerged were confirmed by confirmatory factor analysis. According to the structural equation model results, two out of three emerged factors predicted the outcomes. Conclusions From the initial 42 items, the meta-tool is composed of 20 items capable of predicting the outcomes as with the original tools.RATIONALE There is growing interest in validating tools aimed at supporting the clinical decision-making process and research. However, an increased bureaucratization of clinical practice and redundancies in the measures collected have been reported by clinicians. Redundancies in clinical assessments affect negatively both patients and nurses. METHODS To validate a meta-tool measuring the risks/problems currently estimated by multiple tools used in daily practice. A secondary analysis of a database was performed, using a cross-validation and a longitudinal study designs. In total, 1464 patients admitted to 12 medical units in 2012 were assessed at admission with the Brass, Barthel, Conley and Braden tools. Pertinent outcomes such as the occurrence of post-discharge need for resources and functional decline at discharge, as well as falls and pressure sores, were measured. Explorative factor analysis of each tool, inter-tool correlations and a conceptual evaluation of the redundant/similar items across tools were performed. Therefore, the validation of the meta-tool was performed through explorative factor analysis, confirmatory factor analysis and the structural equation model to establish the ability of the meta-tool to predict the outcomes estimated by the original tools. RESULTS High correlations between the tools have emerged (from r 0.428 to 0.867) with a common variance from 18.3% to 75.1%. Through a conceptual evaluation and explorative factor analysis, the items were reduced from 42 to 20, and the three factors that emerged were confirmed by confirmatory factor analysis. According to the structural equation model results, two out of three emerged factors predicted the outcomes. CONCLUSIONS From the initial 42 items, the meta-tool is composed of 20 items capable of predicting the outcomes as with the original tools.

Flickr as a Tool for the Analysis of Photographic Tourism: The Estimation of Geotagging Rate and Its Use for Mapping the World

The current digital revolution has a strong impact on the people life and on the research process in most of the scientific areas. Geography and cartography have been invested by the innovation connected with GIS. Moreover, they reacted to the new opportunities given by the new web applications founded on the dynamic paradigm of Web 2.0. In the geographic framework the possibility to self-supply the online contents brought to the Volunteered Geographic Information phenomenon. This study presents a number of preliminary results of a project aimed at reformulating the theoretical framework of Photographic Tourism. The research focuses on the integration of social media and web mapping as an application of neogeography. The estimation (based on a sample) of the proportion of geotagged photos loaded on Flickr during 2016 and the analysis of the spatial distribution of the considered sample are the main objectives of the paper.

Lo strumento italiano di misurazione della qualità dell’apprendimento clinico degli studenti infermieri. [The Italian instrument evaluating the nursing students clinical learning quality]

. The Clinical Learning Quality Evaluation Index for nursing students. INTRODUCTION The Italian nursing programs, the need to introduce tools evaluating the quality of the clinical learning as perceived by nursing students. Several tools already exist, however, several limitations suggesting the need to develop a new tool. AIM A national project aimed at developing and validating a new instrument capable of measuring the clinical learning quality as experience by nursing students. METHODS A validation study design was undertaken from 2015 to 2016. All nursing national programs (n=43) were invited to participate by including all nursing students attending regularly their clinical learning. The tool developed based upon a) literature, b) validated tools already established among other healthcare professionals, and c) consensus expressed by experts and nursing students, was administered to the eligible students. RESULTS 9606 nursing in 27 universities (62.8%) participated. The psychometric properties of the new instrument ranged from good to excellent. According to the findings, the tool consists in 22 items and five factors: a) quality of the tutorial strategies, b) learning opportunities; c) safety and nursing care quality; d) self-direct learning; e) quality of the learning environment. CONCLUSIONS The tool is already used. Its systematic adoption may support comparison among settings and across different programs; moreover, the tool may also support in accrediting new settings as well as in measuring the effects of strategies aimed at improving the quality of the clinical learning.: . The Clinical Learning Quality Evaluation Index for nursing students. INTRODUCTION: The Italian nursing programs, the need to introduce tools evaluating the quality of the clinical learning as perceived by nursing students. Several tools already exist, however, several limitations suggesting the need to develop a new tool. AIM: A national project aimed at developing and validating a new instrument capable of measuring the clinical learning quality as experience by nursing students. METHODS: A validation study design was undertaken from 2015 to 2016. All nursing national programs (n=43) were invited to participate by including all nursing students attending regularly their clinical learning. The tool developed based upon a) literature, b) validated tools already established among other healthcare professionals, and c) consensus expressed by experts and nursing students, was administered to the eligible students. RESULTS: 9606 nursing in 27 universities (62.8%) participated. The psychometric properties of the new instrument ranged from good to excellent. According to the findings, the tool consists in 22 items and five factors: a) quality of the tutorial strategies, b) learning opportunities; c) safety and nursing care quality; d) self-direct learning; e) quality of the learning environment. CONCLUSIONS: The tool is already used. Its systematic adoption may support comparison among settings and across different programs; moreover, the tool may also support in accrediting new settings as well as in measuring the effects of strategies aimed at improving the quality of the clinical learning.