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Postdoctoral scholars are increasingly pursuing diverse career paths requiring broad skill sets. This study aims to create a more comprehensive understanding of current approaches and strategies for postdoctoral scholars professional learning and development.

This literature review is a systematic examination and synthesis of the current literature describing professional learning and development pertaining to postdoctoral scholars. The objectives and components of initiatives were extracted and narratively synthesized to identity important patterns and themes across the literature.

Commonalities amongst professional learning and development initiatives for postdoctoral scholars included skills development in the following areas: teaching and learning, mentorship, academic careers, academic writing, industry careers, networking, career planning, project management, time management, communication, leadership and balancing work-life demands.

In synthesizing the literature that describes professional learning and development opportunities for postdoctoral scholars, it is apparent that opportunities look different in every setting with no empirical evidence that one strategy is more effective than another. Given the significant resources often required to support professional learning and development initiatives, a deeper understanding of the benefits and deficiencies of various components is needed to ensure scarce resources are invested in the most effective strategies.

Using Johansson-Sköldberg et al.’s (2013) descriptions of design discourses, this study aims to analyze teacher interviews, research notes and teacher and student artifacts to determine if engagement in design practices led to changes in the teacher’s thinking.

This article presents results from a year-long study that explored how a teacher enacted design discourses to engage in curriculum learning within an elementary school makerspace. The design-based study involved a collaborative partnership where a teacher and researcher co-designed, co-enacted and co-reflected on three cycles of making featuring curriculum studies in science, mathematics and social studies.

The authors determined that engagement in all four design discourses led to transformative changes in the teacher’s thinking about herself as a teacher and her students as learners. The evidence suggests the school makerspace can serve as a liminal design space for professional learning, given that implicit in the makerspace is the embodiment of design practices such as problem finding, iteration and reflection.

Engaging in design discourses in the makerspace can lead teachers to question the frames they hold about teaching and learning. However, teachers need ongoing support in developing discipline knowledge and prioritizing the time required for designing, iterating and reflecting on learning in the makerspace.

The makerspace provides a liminal space for teachers’ professional learning in that implicit in the makerspace is the embodiment of design practices such as problem finding, iteration and reflection.

This study is unique, in that it places the importance of teacher learning in the elementary school makerspace on equal footing with student learning, thereby creating a culture of inquiry for all.

Student mental well-being is a matter of increasing concern on university campuses around the world. Social, psychological, academic and career aspects of graduate learning are enriched through peer mentorship. Peer-mentoring experiences and the impacts of these relationships on the mental well-being of graduate students remain underexplored in the scholarship of teaching and learning. The purpose of this study was to explore how engagement in formal and informal peer mentorship, as described by students across four academic disciplines, impacts the social connectedness and well-being of graduate students.

A convergent mixed methods research design was used, with quantitative and qualitative data gathered in parallel to gain a comprehensive, corroborated and integrated understanding of graduate students’ perspectives and experiences with peer mentorship. Online survey and interview data were collected from graduate thesis-based master’s EdD and PhD students in education, medicine, nursing and social work. The data were analyzed using descriptive statistics and thematic analysis.

The authors found a commonality of graduate student experiences across disciplines with respect to the diverse psychosocial impacts of graduate peer mentorship. Peer-mentoring relationships offered mentees emotional support, motivation and a sense of community and offered mentors opportunities for self-development and gratification.

This research is unique in its in-depth exploration of the interdisciplinary perspectives and experiences of graduate students from Education, Nursing, Medicine and Social Work. While further research is needed to explore the implementation of structural approaches to support the development of peer-mentoring relationships in graduate education, the multidisciplinary focus and depth and breadth of this inquiry suggest the potential transferability of the study findings to other disciplines and academic settings. The findings from this study further highlight the need for strategic activation of existing program resources to foster greater connectedness and well-being among graduate students.

Many postdoctoral scholars are seeking professional learning and development (PLD) opportunities to prepare for diverse careers, roles and responsibilities. This paper aims to develop an evidence-informed framework for PLD of postdoctoral scholars that speaks to these changing career paths.

This paper used an integrated knowledge translation approach to synthesize and extend previous work on postdoctoral scholars’ PLD. The authors engaged in consultations with key stakeholders and synthesized findings from literature reviews, surveys and semi-structured interviews to create a framework for PLD.

The PLD framework consists of four major domains, namely, professional socialization; professional skills; academic development; and personal effectiveness. The 4 major domains are subdivided into 16 subdomains that represent the various skills and competencies that postdoctoral scholars can build throughout their postdoctoral fellowships.

The framework can be used to support postdoctoral scholars, postdoctoral supervisors and higher education institutions in developing high quality, evidence-informed PLD plans to meet the diverse career needs of postdoctoral scholars.

This paper examines current issues related to technology integration and provides a contextual framework with which school principals can undertake new leadership responsibilities in this area. Selected examples of successful technology integration are provided to inform current technology leadership practices. The leadership goals, competencies and responsibilities needed in order to achieve this preferred future are described. In the final section, the authors draw on professional experiences as researcher and teacher/leader to build and expand on a five‐part leadership model currently in use by a large urban school district to interpret multiple dimensions of technology leadership for principals. Ways in which this framework can serve as a guide for school leaders as they develop technology competencies, implement professional growth plans, work with their community, and provide daily technology leadership, mentorship and advocacy for teachers in an elementary school are discussed.

The purpose of this paper is to examine the drug use and criminal justice factors related to hepatitis C virus (HCV) antibody reactivity among rural women in the USA recruited from local jails.

Analyses included 277 women with a history of injection drug use from three rural jails in Kentucky. Participants completed health and drug use questionnaires and received antibody testing for HCV.

The majority of women tested reactive to the HCV antibody (69 percent). Reactivity was associated with risk factors, such as unsterile needle use. Criminal justice variables, including an increased likelihood of prison incarceration, an earlier age of first arrest, and a longer incarceration history, were associated with HCV reactive tests. Participants also endorsed several barriers to seeking healthcare before entering jail that were more prevalent in women testing HCV reactive regardless of HCV status awareness before entering jail.

Injection and high-risk sharing practices as well as criminal justice factors were significantly associated with HCV reactivity. Future research and practice could focus on opportunities for linkages to HCV treatment during incarceration as well as during community re-entry to help overcome real or perceived treatment barriers. The current study highlights the importance of the criminal justice system as a non-traditional, real-world setting to examine drug use and related health consequences such as HCV by describing the association of high-risk drug use and criminal justice consequences with HCV among rural women recruited from local jails.

The aim of this article is to expound a holistic intellectual capital index. Moreover, the paper presents its implementation within a real estate organization.

The index is calculated on the basis of 14 structured interviews synthesized through the analytic hierarchy process, min–max normalizations and weighted sums.

The authors estimate a holistic intellectual capital index and show that an analysis of its components may allow identifying discordances within the organization about the contributions of its value drivers.

The proposed index may be used in a medium-/long-term research to measure the evolution in the organizational intellectual capital and its relation with the top management’s initiatives and the competitive environment changes.

The proposed methodology may integrate the reports prepared for shareholders and stakeholders and provide the top management, with an in-depth understanding of the different perceptions of the organizational human resources.

The authors discuss a holistic index of intellectual capital that allows considering both the performances of the intellectual capital components and the interdependencies among them and also their strategic contribution to the value-creation process. In addition, they propose a novel descriptive statistical analysis of the assessment and management of IC-index components to draw indications for the top management.

The purpose of this study is to suggest a new framework that we call the CIR#, which allows forecasting interest rates from observed financial market data even when rates are negative. In doing so, we have the objective is to maintain the market volatility structure as well as the analytical tractability of the original CIR model.

The novelty of the proposed methodology consists in using the CIR model to forecast the evolution of interest rates by an appropriate partitioning of the data sample and calibration. The latter is performed by replacing the standard Brownian motion process in the random term of the model with normally distributed standardized residuals of the “optimal” autoregressive integrated moving average (ARIMA) model.

The suggested model is quite powerful for the following reasons. First, the historical market data sample is partitioned into sub-groups to capture all the statistically significant changes of variance in the interest rates. An appropriate translation of market rates to positive values was included in the procedure to overcome the issue of negative/near-to-zero values. Second, this study has introduced a new way of calibrating the CIR model parameters to each sub-group partitioning the actual historical data. The standard Brownian motion process in the random part of the model is replaced with normally distributed standardized residuals of the “optimal” ARIMA model suitably chosen for each sub-group. As a result, exact CIR fitted values to the observed market data are calculated and the computational cost of the numerical procedure is considerably reduced. Third, this work shows that the CIR model is efficient and able to follow very closely the structure of market interest rates (especially for short maturities that, notoriously, are very difficult to handle) and to predict future interest rates better than the original CIR model. As a measure of goodness of fit, this study obtained high values of the statistics R² and small values of the root of the mean square error for each sub-group and the entire data sample.

A limitation is related to the specific dataset as we are examining the period around the 2008 financial crisis for about 5 years and by using monthly data. Future research will show the predictive power of the model by extending the dataset in terms of frequency and size.

Improved ability to model/forecast interest rates.

The original value consists in turning the CIR from modeling instantaneous spot rates to forecasting any rate of the yield curve.

The following is an annotated list of materials dealing with information literacy including instruction in the use of information resources, research, and computer skills related to retrieving, using, and evaluating information. This review, the twenty‐second to be published in Reference Services Review, includes items in English published in 1995. After 21 years, the title of this review of the literature has been changed from “Library Orientation and Instruction” to “Library Instruction and Information Literacy,” to indicate the growing trend of moving to information skills instruction.

The purpose of this paper is to model interest rates from observed financial market data through a new approach to the Cox–Ingersoll–Ross (CIR) model. This model is popular among financial institutions mainly because it is a rather simple (uni-factorial) and better model than the former Vasicek framework. However, there are a number of issues in describing interest rate dynamics within the CIR framework on which focus should be placed. Therefore, a new methodology has been proposed that allows forecasting future expected interest rates from observed financial market data by preserving the structure of the original CIR model, even with negative interest rates. The performance of the new approach, tested on monthly-recorded interest rates data, provides a good fit to current data for different term structures.

To ensure a fitting close to current interest rates, the innovative step in the proposed procedure consists in partitioning the entire available market data sample, usually showing a mixture of probability distributions of the same type, in a suitable number of sub-sample having a normal/gamma distribution. An appropriate translation of market interest rates to positive values has been introduced to overcome the issue of negative/near-to-zero values. Then, the CIR model parameters have been calibrated to the shifted market interest rates and simulated the expected values of interest rates by a Monte Carlo discretization scheme. We have analysed the empirical performance of the proposed methodology for two different monthly-recorded EUR data samples in a money market and a long-term data set, respectively.

Better results are shown in terms of the root mean square error when a segmentation of the data sample in normally distributed sub-samples is considered. After assessing the accuracy of the proposed procedure, the implemented algorithm was applied to forecast next-month expected interest rates over a historical period of 12 months (fixed window). Through an error analysis, it was observed that our algorithm provides a better fitting of the predicted expected interest rates to market data than the exponentially weighted moving average model. A further confirmation of the efficiency of the proposed algorithm and of the quality of the calibration of the CIR parameters to the observed market interest rates is given by applying the proposed forecasting technique.

This paper has the objective of modelling interest rates from observed financial market data through a new approach to the CIR model. This model is popular among financial institutions mainly because it is a rather simple (uni-factorial) and better model than the former Vasicek model (Section 2). However, there are a number of issues in describing short-term interest rate dynamics within the CIR framework on which focus should be placed. A new methodology has been proposed that allows us to forecast future expected short-term interest rates from observed financial market data by preserving the structure of the original CIR model. The performance of the new approach, tested on monthly data, provides a good fit for different term structures. It is shown how the proposed methodology overcomes both the usual challenges (e.g. simulating regime switching, clustered volatility and skewed tails), as well as the new ones added by the current market environment (particularly the need to model a downward trend to negative interest rates).