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Conversation Analysis (CA) is one of the predominant methods for the detailed study of human social interaction. Bringing together thirty-four chapters written by a team of world-renowned experts, this Handbook represents the first comprehensive overview of conversation-analytic methods. Topics include how to collect, manage, and transcribe data; how to explore data in search of possible phenomena; how to form and develop collections of phenomena; how to use different types of evidence to analyze data; how to code and quantify interaction; and how to apply, publish, and communicate findings to those who stand to benefit from them. Each method is introduced clearly and systematically, and examples of CA in different languages and cultures are included, to show how it can be applied in multiple settings. Comprehensive yet accessible, it is essential reading for researchers and advanced students in disciplines such as Linguistics, Sociology, Anthropology, Communication and Psychology.
This chapter discusses different types of evidence that conversation analysts use to ground their claims about social action. We begin by reviewing the epistemological perspective of CA, which demands that evidence reflect participants’ orientations; as a critical part of understanding the terms ‘participant orientation’ and ‘relevance,’ here we also discuss two ways in which CA’s position and emphasis on them are commonly misunderstood. The bulk of this chapter then reviews and illustrates a range of types of participant-orientation evidence. We organize our presentation of types of evidence roughly by sequential position vis-à-vis the focal action about which the analyst is making claims, including evidence to be found in: (i) next-turn, (ii) same-turn (e.g., same-TCU self-repair, accounts), (iii) prior turn or sequence, (iv) third turn/position (e.g., repair after next turn, courses of action/activity), (v) fourth turn/position, and (vi) more distal positions. We also discuss other forms of evidence that are not necessarily defined by sequential position, including: (i) third-party conduct, (ii) reported conduct, (iii) deviant cases, and (iv) distributional evidence. We conclude by offering some brief reflections on bringing different types and positions of evidence together toward the construction of an argument.
While the preceding chapters of the Handbook have focused on practical skills in CA research methods, this chapter looks towards the path ahead. A diverse group of conversation analysts were asked to outline possible projects, point readers toward un- or under-described interactional phenomena, and discuss persistent issues in the field. The contributions address future advances in data collection, specific interactional practices, the complex interplay between language and the body, and cross-cultural and crosslinguistic comparisons, among other issues. The chapter concludes with a concise reiteration of the bedrock principle that underpins all CA research methods.
This chapter provides an overview of foundational principles that guide CA research, offered both on the basis of our own experiences as researchers, and from our discussions with other conversation analysts as they authored contributions for the present volume. We begin by briefly sketching of some of the fundamentals of human social interaction, in order to underscore CA’s central focus, the study of social action, and describe some of the basic features of how interaction is procedurally organized. These basic features of interaction, which CA research has rigorously evidenced and which guide our examination of new data, are then shown directly to inform CA as a research methodology. Put another way, it is precisely due to the procedural infrastructure of action in interaction that conversation analysts use and work with interactional data in particular ways. We conclude with advice for readers as they continue to explore the volume’s contents.
Evaluation of adult antibiotic order sets (AOSs) on antibiotic stewardship metrics has been limited. The primary outcome was to evaluate the standardized antimicrobial administration ratio (SAAR). Secondary outcomes included antibiotic days of therapy (DOT) per 1,000 patient days (PD); selected antibiotic use; AOS utilization; Clostridioides difficile infection (CDI) cases; and clinicians’ perceptions of the AOS via a survey following the final study phase.
Design:
This 5-year, single-center, quasi-experimental study comprised 5 phases from 2017 to 2022 over 10-month periods between August 1 and May 31.
Setting:
The study was conducted in a 752-bed tertiary care, academic medical center.
Intervention:
Our institution implemented AOSs in the electronic medical record (EMR) for common infections among hospitalized adults.
Results:
For the primary outcome, a statistically significant decreases in SAAR were detected from phase 1 to phase 5 (1.0 vs 0.90; P < .001). A statistically significant decreases were detected in DOT per 1,000 PD (4,884 vs 3,939; P = .001), fluoroquinolone orders (407 vs 175; P < .001), carbapenem orders (147 vs 106; P = .024), and clindamycin orders (113 vs 73; P = .01). No statistically significant change in mean vancomycin orders was detected (991 vs 902; P = .221). A statistically significant decrease in CDI cases was also detected (7.8, vs 2.4; P = .002) but may have been attributable to changes in CDI case diagnosis. Clinicians indicated that the AOSs were easy to use overall and that they helped them select the appropriate antibiotics.
Conclusions:
Implementing AOS into the EMR was associated with a statistically significant reduction in SAAR, antibiotic DOT per 1,000 PD, selected antibiotic orders, and CDI cases.
Competition between genotypes within a plant population can result in the displacement of the least competitive by more competitive genotypes. Although evolutionary processes in plants may occur over thousands and millions of years, it has been suggested that changes in key fitness traits could occur in as little as decades, with herbicide resistance being a common example. However, the rapid evolution of complex traits has not been proven in weeds. We hypothesized that changes in weed growth and competitive ability can occur in just a few years because of selection in agroecosystems. Seed of multiple generations of a single natural population of the grassy weed giant foxtail (Setaria faberi Herrm.) were collected during 34 yr (i.e., 1983 to 2017). Using a “resurrection” approach, we characterized life-history traits of the different year-lines under noncompetitive and competitive conditions. Replacement-series experiments comparing the growth of the oldest year-line (1983) versus newer year-lines (1991, 1996, 1998, 2009, and 2017) showed that plant competitive ability decreased and then increased progressively in accordance with oscillating selection. The adaptations in competitive ability were reflected in dynamic changes in leaf area and biomass when plants were in competition. The onset of increased competitive ability coincided with the introduction of herbicide-resistant crops in the landscape in 1996. We also conducted a genome-wide association study and identified four loci that were associated with increased competitive ability over time, confirming that this trait changed in response to directional selection. Putative transcription factors and cell wall–associated enzymes were linked to those loci. This is the first study providing direct in situ evidence of rapid directional evolution of competitive ability in a plant species. The results suggest that agricultural systems can exert enough pressure to cause evolutionary adaptations of complex life-history traits, potentially increasing weediness and invasiveness.