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To better understand the evaluation practices set by the Bologna process as well as their properties and implications, this paper critically explores the judgements of quality in external evaluations in higher education with regard to their orientation towards three phases: conditions, processes and end states. It furthermore explores how this modality is connected with how critical external evaluations are. It develops a theoretical framework for observing evaluation practices to support the statistical analysis of external evaluation reports for 485 study programmes in Slovenian higher education. The findings offer insight into how quality assurance impacts higher education in practice, considering that the subsequent measures taken by the higher education institutions correspond with the outcomes of the evaluations.

The main purpose of this research is to analyse school principals' and teachers' attitudes towards external school evaluation in Iceland, in particular, the ways in which they consider the evaluation affects their schools' internal evaluation and drives changes in their own practices. The study uses a quantitative method and is based on a survey conducted among principals and teachers in 22 schools that were externally evaluated during the years 2013 to 2015. The results indicate a positive attitude towards external school evaluation among both teachers and principals. Acceptance, setting expectations, and teacher participation were found to be significant predictors of perceived changes in internal evaluation in the teachers' data. However, only acceptance significantly explained perceived changes in teaching practices. In the principals' data, the only variable that had a significant association with perceived changes in internal evaluation was setting expectations, and only acceptance had a significant association with perceived changes in leadership practices. In accordance with the hypothesis of this study, the results underpin the importance of acceptance of the evaluation feedback and setting expectations through quality standards. However, contrary to the hypothesis, external stakeholder involvement did not prove to be a strong determinant of change as perceived by principals.

Water research projects are often interdisciplinary and participatory in nature. Scientists and managers involved strive to create learning that advances science in various fields while providing benefits for society. They also face challenges of monitoring and evaluation (M&E) for accountability and measurement of impacts, required by funding agencies. In this paper we tested selected M&E processes and methods for their potential to increase capacity in a cross-cultural inter-disciplinary research setting of an urban water research project. A total of eight different qualitative and quantitative evaluation methods were assessed using two processes: self-evaluation by researchers, testing five methods; and the external evaluations by stakeholders, testing three methods. Both processes were found useful. Of the five self-evaluation methods tested, the Logical Frameworks method was deemed a good planning tool but not one contributing to learning. The qualitative (Factors of Success and Obstacles/Enablers) and the Best Case/Worst Case Scenarios quantitative method were effective learning methods. Tracking Change was found suitable for measuring learning. We recommended that a combination of methods be used in order to capture the full dynamic of projects in various stages. Explicit timelines for indicators and measures of project success are recommended for managing expectations of both researchers and funding agencies.

Species distribution models (SDM) have been increasingly developed in recent years, but their validity is questioned. Their assessment can be improved by the use of independent data, but this can be difficult to obtain and prohibitive to collect. Standardized data from citizen science may be used to establish external evaluation datasets and to improve SDM validation and applicability. We used opportunistic presence‐only data along with presence–absence data from a standardized citizen science program to establish and assess habitat suitability maps for 9 species of amphibian in western France. We assessed Generalized Additive and Random Forest Models’ performance by (1) cross‐validation using 30% of the opportunistic dataset used to calibrate the model or (2) external validation using different independent datasets derived from citizen science monitoring. We tested the effects of applying different combinations of filters to the citizen data and of complementing it with additional standardized fieldwork. Cross‐validation with an internal evaluation dataset resulted in higher AUC (Area Under the receiver operating Curve) than external evaluation causing overestimation of model accuracy and did not select the same models; models integrating sampling effort performed better with external validation. AUC, specificity, and sensitivity of models calculated with different filtered external datasets differed for some species. However, for most species, complementary fieldwork was not necessary to obtain coherent results, as long as the citizen science data were strongly filtered. Since external validation methods using independent data are considered more robust, filtering data from citizen sciences may make a valuable contribution to the assessment of SDM. Limited complementary fieldwork with volunteer's participation to complete ecological gradients may also possibly enhance citizen involvement and lead to better use of SDM in decision processes for nature conservation. To develop new scientific and participatory approaches might better integrate biodiversity issues into decision‐making processes. In this study, we used different datasets to establish and assess habitat suitability maps for 9 species of amphibian in France. We compared different SDM as well as the effect of integrating parameters relating to sampling effort. We assessed the models’ performance by (1) internal cross‐validation or (2) external validation using an independent dataset from citizen science monitoring strategically completed. The AUC values obtained with cross‐validation were much higher for all species than with external evaluation and models integrating sampling effort performed better with external validation methods. Data from citizen science may help to get valuable external dataset to the assessment of SDM.

Researchers have endeavored to explain the causes of short organizational time horizons because of the organizational and societal costs of corporate short-termism. These explanations, however, tend to confound cognitive with behavioral explanations, which masks the importance of cognitive biases. We address this oversight by situating our work in prospect theory and organizational search, which underscores the importance of external evaluations on organizational time horizons and the asymmetry of positive and negative evaluations. Specifically, we argue that negative evaluations will shorten organizational time horizons more than positive evaluations will lengthen them. In our research context of financial analysts, this means that “sell” recommendations will shorten time horizons more than “buy” recommendations will lengthen them. Our main thesis can help to explain rising short-termism among some publicly traded companies. We operationalize organizational time horizons by the language managers use during 3,136 quarterly earnings conference calls. We test our main hypothesis and other timing-related moderating effects on 98 extractives firms from 2006 to 2013.

Background Antimicrobial resistance (AMR) is of growing concern globally and AMR status in sub-Saharan Africa (SSA) is undefined due to a lack of real-time data recording, surveillance and regulation. World Health Organization (WHO) Joint External Evaluation (JEE) reports are voluntary, collaborative processes to assess country capacities and preparedness to prevent, detect and rapidly respond to public health risks, including AMR. The data from SSA JEE reports were analysed to gain an overview of how SSA is working towards AMR preparedness and where strengths and weaknesses lie. Methods SSA country JEE AMR preparedness scores were analysed. A cumulative mean of all the SSA country AMR preparedness scores was calculated and compared to the overall mean SSA JEE score. AMR preparedness indicators were analysed, and data were weighted by region. Findings The mean SSA AMR preparedness score was 53% less than the overall mean SSA JEE score. East Africa had the highest percentage of countries reporting having AMR National Action Plans in place, as well as human and animal pathogen AMR surveillance programmes. Southern Africa reported the highest percentage of countries with training programmes and antimicrobial stewardship. Conclusions The low mean AMR preparedness score compared to overall JEE score, along with the majority of countries lacking implemented National Action Plans, suggests that until now AMR has not been a priority for most SSA countries. By identifying regional and One Health strengths, AMR preparedness can be fortified across SSA with a multisectoral approach.

Abstract With the rapid acceleration of changes being experienced throughout the world and in particular within health and health and social care, accreditation programmes must keep pace or go the way of the dinosaur. While accreditation has deep roots in some countries, in the past 30 years, it has spread to a considerably larger range of countries in a mix of mandatory and voluntary systems. Accreditation is a tool to improve the quality of healthcare and social care, and in particular, there is recent recognition of its value in low- and middle-income countries, with promotion by the World Health Organization (WHO). The challenge is that with the rapid pace of change, how does accreditation reframe and reposition itself to ensure relevance in 2030? Accreditation must adapt and be relevant in order to be sustainable. This article outlines the fundamental principles, reviews the global trends’ impact on accreditation and the challenges with the existing model and, through the lens of living in 2030, outlines how accreditation programmes will be structured and applied 10 years from now.

Providing high quality and safe patient care is a challenge in the current rapidly changing and complex health care environment. A variety of independent tools and methodologies contribute to this effort, e.g. regulatory requirements, quality improvement tools and accreditation methodologies. A concern is that each alone will not achieve the tipping point in health care quality that is required. This paper suggests that the methodology and application of accreditation have the potential to be the force to bring these approaches into alignment and ultimately measurably improve the quality of care. 在现今快速变迁及复杂的医疗照护环境下，提供高质量及安全病人照护是一项挑战。多样的独立工具及研究方法努力促成，像是监管要求、质量改善工具及评鉴方法。担忧的是各项独自无法达到健康照护质量所要求的临界点。本篇文章提出研究方法及评鉴的应用有潜力将这些方法标准化，最终显著地改善照护质量。 Proveer una atención de alta calidad y seguridad al paciente es un reto en el actual rápidamente cambiante y complejo entorno de los servicios de salud. Una variedad de herramientas independientes y metodologías contribuyen a este esfuerzo p.ej. requerimientos regulatorios, herramientas de mejora de la calidad y metodologías de acreditación. Una preocupación es que cada una por separado no consiga alcanzar el nivel en la calidad de la atención de salud que se requiere. Este artículo sugiere que la metodología y aplicación de la acreditación tiene el potencial para ser la fuerza que alinee estosenfoquesy, endefinitiva, mejorar de manera cuantificable la calidad de la atención. 在現今快速變遷及複雜的醫療照護環境下，提供高品質及安全病人照護是一項挑戰。多樣的獨立工具及研究方法努力促成，像是監管要求、品質改善工具及評鑑方法。擔憂的是各項獨自無法達到健康照護品質所要求的臨界點。本篇文章提出研究方法及評鑑的應用有潛力將這些方法標準化，最終顯著地改善照護品質。 Prover cuidado de saúde de alTa qualidade e seguro para o paciente é um desafio no ambiente complexo e de mudanças rápidas corrente. Uma variedade de ferramentas e metodologias independentes contribuem nesse sentido, ex. requisitos regulatórios, ferramentas de melhoria da qualidade e metodologias de acreditação. Uma preocupação é que cada uma separadamente não alcançará a mudança necessária na qualidade do cuidado de saúde. Este artigo sugere que a metodologia e aplicação da acreditação têm o potencial de ser a força que faz com que essas abordagens se alinhem e, em última instância, melhorem de modo sensível a qualidade do cuidado. 高品質かつ患者にも安全なケアを提供することは近 年の急速に変化し、複雑性の中にある医療をとりま く環境においては課題となっている。様々な独立し たツールと方法がこの取り組みに貢献している （例：規制上の要件、質の改善ツール、認証評価 法）。懸念すべき点は、それぞれ単独では求められ る医療の質の転換点までは達しないだろうというこ とである。本稿は認証評価の方法と適用はこれらの アプローチを調整して最終的にはケアの質を適度に 改善する可能性を秘めていると提案している。 Dispenser des soins d’un haut niveau de qualité et de sécurité est un défi dans le contexte actuel d’évolution rapide et complexe des soins de santé. Divers outils et méthodes, indépendants les uns des autres, contribuent à cet objectif, parmi lesquels on peut citer les exigences du régulateur, les outils et méthodes d’amélioration de la qualité et l’accréditation. Il est préoccupant de constater que chacune de ces approches, prise isolément, ne suffit pas à atteindre les améliorations attendues. Cet article suggère que la méthodologie et la pratique de l’accréditation permettent de potentialiser les différentes approches et de les aligner pour, en fin de compte, améliorer de façon mesurable la qualité des soins.

Routine clinical meropenem therapeutic drug monitoring data can be applied to model-informed precision dosing. The current study aimed to evaluate the adequacy and predictive capabilities of the published models with routine meropenem data and identify the dosing adaptations using a priori and Bayesian estimation. For this, 14 meropenem models for the external evaluation carried out on an independent cohort of 134 patients with 205 meropenem concentrations were encoded in NONMEM 7.3. The performance was determined using: 1) prediction-based and simulation-based diagnostics; and 2) predicted meropenem concentrations by a priori prediction using patient covariates only; and Bayesian forecasting using previous observations. The clinical implications were assessed according to the required dose adaptations using the meropenem concentrations. All assessments were stratified based on the patients with or without continuous renal replacement therapy. Although none of the models passed all tests, the model by Muro et al. showed the least bias. Bayesian forecasting could improve the predictability over an a priori approach, with a relative bias of −11.63–68.89% and −302.96%–130.37%, and a relative root mean squared error of 34.99–110.11% and 14.78–241.81%, respectively. A dosing change was required in 40.00–68.97% of the meropenem observation results after Bayesian forecasting. In summary, the published models couldn’t adequately describe the meropenem pharmacokinetics of our center. Although the selection of an initial meropenem dose with a priori prediction is challenging, the further model-based analysis combining therapeutic drug monitoring could be utilized in the clinical practice of meropenem therapy.

ObjectivesSeveral population pharmacokinetics (popPK) models for polymyxin B have been constructed to optimize therapeutic regimens. However, their predictive performance remains unclear when extrapolated to different clinical centers. Therefore, this study aimed to evaluate the predictive ability of polymyxin B popPK models.MethodsA literature search was conducted, and the predictive performance was determined for each selected model using an independent dataset of 20 patients (92 concentrations) from the Third Xiangya Hospital. Prediction- and simulation-based diagnostics were used to evaluate model predictability. The influence of prior information was assessed using Bayesian forecasting.ResultsEight published studies were evaluated. In prediction-based diagnostics, the prediction error within ± 30% was over 50% in two models. In simulation-based diagnostics, the prediction- and variability-corrected visual predictive check (pvcVPC) showed satisfactory predictivity in three models, while the normalized prediction distribution error (NPDE) tests indicated model misspecification in all models. Bayesian forecasting demonstrated a substantially improvement in the model predictability even with one prior observation.ConclusionNot all published models were satisfactory in prediction- and simulation-based diagnostics; however, Bayesian forecasting improved the predictability considerably with priors, which can be applied to guide polymyxin B dosing recommendations and adjustments for clinicians.