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Scholarly communication in the U.S. has been closely examined in the past two decades by librarians because of the acceleration in costs of serial, scholarly communication. Specific disciplines of research have increased at unprecedented rates, namely the areas of scientific, technical, and medical (STM) publishing.

Weeding is, among all of the tasks common in librarianship, one of the most rife with conflict. The purpose of this chapter was to examine the emotions and emotional impact of deselection on librarians and library staff using a framework of weeding styles. The authors did a qualitative survey-based case study. They created a survey using Google Forms and deployed it to five library-related listservs and one Facebook group. All of the questions were mandatory, except for the open-answer questions. The authors also conducted an extensive literature review. The survey revealed a more harmonious practice than the literature might indicate. There were some noteworthy correlations drawn among weeding styles, emotions, and the practice of weeding. The authors discuss the implications of their findings and ideas for future research. They also provide strategies and recommendations in terms of communication in the course of deselection and how to handle emotional labor in the workplace. A literature review conducted by both authors did not reveal any other examination of this type. The chapter will fill a gap in the literature.

In recent years, a growing number of libraries have canceled or unbundled their “Big Deal” journal subscriptions – those subscriptions that include a full package of digital journal titles for one discounted cost. This started as an affordability problem but has slowly morphed into a challenge from libraries demanding a new pricing structure that accommodates and spurs the growing open access movement.

The change has caused a variety of challenges for technical services units including the increased need for user data, increasingly complicated workflows as they manage partial subscriptions, new interactions with consortia, and ongoing campus conversations. Whether the library is seeking to simply unbundle due to budget constraints, or push for new models such as “read and publish”, there is a tremendous impact on the work of technical services units.

This chapter will explore the rationale and growth of the Big Deal, how it is breaking, four case studies on breaking Big Deals, a brief discussion of new transformative agreements, new challenges for consortia, and implications for technical services units moving forward.

Health care institutions in many Western countries have developed preoperative testing and assessment guidelines to improve surgical outcomes and reduce cost of surgical care. The aims of this chapter are to (1) summarize the literature on the effect of preoperative testing on clinical outcomes, efficiency, and cost; and (2) to compare preoperative testing guidelines developed in the United States, the United Kingdom, and Canada.

We reviewed the literature from 1975 to 2014 for studies and preoperative testing guidelines.

We identified 29 empirical studies and 8 country-specific guidelines for review. Most studies indicate that preoperative testing is overused and comes at a high cost. Guidelines are tied to payment only in one country studied. This is the most recent review of the literature on preoperative testing and assessment with a focus on quality of care, efficiency, and cost outcomes. In addition, this chapter provides an international comparison of preoperative guidelines.

Standard estimation of ARMA models in which the AR and MA roots nearly cancel, so that individual coefficients are only weakly identified, often produces inferential ranges for individual coefficients that give a spurious appearance of accuracy. We remedy this problem with a model that uses a simple mixture prior. The posterior mixing probability is derived using Bayesian methods, but we show that the method works well in both Bayesian and frequentist setups. In particular, we show that our mixture procedure weights standard results heavily when given data from a well-identified ARMA model (which does not exhibit near root cancellation) and weights heavily an uninformative inferential region when given data from a weakly-identified ARMA model (with near root cancellation). When our procedure is applied to a well-identified process the investigator gets the “usual results,” so there is no important statistical cost to using our procedure. On the other hand, when our procedure is applied to a weakly identified process, the investigator learns that the data tell us little about the parameters – and is thus protected against making spurious inferences. We recommend that mixture models be computed routinely when inference about ARMA coefficients is of interest.

Purpose: The COVID-19 global outbreak has delineated a scenario characterized by lockdown measures, travel bans, unemployment, cancelled bookings and limited travel confidence and logistics, thus forcing many tourism destinations and related tourism businesses to cease operations either partially or totally. Empirical studies aiming to assess the impacts of COVID-19 on the tourism and hospitality industry, as well as deepen our understanding about the main response actions that tourism stakeholders consider to be relevant to cope with the crisis, are extremely relevant. This is even more true in the context of island tourism destinations, where little research has addressed these issues so far. This study was therefore carried out to contribute to filling this research gap by presenting and discussing findings of an empirical research conducted on the accommodation sector in the Island of Sardinia (Italy) with the aim to investigate the impacts that the COVID-19 outbreak has generated on their booking and employment recruitment. Further, it also aims to identify and examine the most important response actions accommodation providers are implementing to cope with the situation, and what type of interventions they think regional/national institutions should adopt to sustain them.

Design/Methodology/Approach: A convergent mixed-method approach (data validation variant) was applied on a sample of 300 complete questionnaires collected in the period 15-30 March 2020, which included 140 narratives to be used for content analysis.

Findings: Quantitative findings report high cancellation rates, a severe decline in reservations and a relevant downsizing in staff recruitment. Qualitative findings identify several macro-based (i.e., institutional) and micro-based (i.e., business) interventions. Macro-based interventions are related to financial, fiscal, health, welfare, transport, destination management and destination marketing aspects. Micro-based interventions are mostly related to innovation, sanitation and cleanliness and pricing/cancellation policies.

Originality/Value: The chapter adds to the still limited island-related research aiming to assess the impacts of COVID-19 on the tourism and hospitality industry and to deepen the scientific debate about the main response actions that accommodation providers consider to be relevant to cope with the crisis.