Class Of Preferences Research Articles

What Matters Most for Long-Acting Antiretroviral Therapy? A Best-Worst Scaling Discrete Choice Experiment.

Florida remains a high-incidence, high-prevalence setting for HIV. Long-acting (LA) antiretroviral therapies (ART) could improve HIV-related outcomes and reduce transmission. This study identifies preferred LA ART characteristics and classes of preference among people with HIV (PWH) in Florida. The Florida Cohort enrolls adult PWH from six counties. In February 2023, a best-worst scaling discrete choice experiment (BWDCE) was added which included 12 tasks with three alternatives and an opt-out (i.e., their current regimen). Six attributes were included: treatment type (e.g., shot), long-term effects, side effects, location (e.g., at home), effectiveness, and frequency. A Hierarchical Bayes model was used to estimate level utilities, attribute importance was calculated, and a latent class model was run in Sawtooth Software. Overall, 208 PWH participated (60% aged 50+, 49% non-Hispanic Black, 54% male). Treatment type had the greatest impact on preference [27.2% (95%CI 25.1-29.3)], followed by frequency [23.4% (95%CI 21.6-25.2)], and long-term effects [19.0% (95%CI 17.8-20.3)]. Within treatment type, LA pills were preferred over other options, including their current regimen. Less frequent administration was preferred, but only yearly administration was preferred over their current regimen. Within long-term effects, participants preferred no increase in risk. Two classes were identified where one class (27% of participants) preferred their current regimen and the other (73% of participants) preferred an alternative, placing greater importance on frequency. PWH preferred LA pills and less frequent administration, so future ART development could focus on options with these traits. Further exploration of user preference classes is needed.

Identifying Preferred Features of Influenza Vaccination Programs Among Chinese Clinicians Practicing Traditional Chinese Medicine and Western Medicine: Discrete Choice Experiment.

Achieving high vaccine coverage among clinicians is crucial to curb the spread of influenza. Traditional Chinese medicine (TCM), rooted in cultural symbols and concepts without direct parallels in modern Western medicine, may influence perspectives on vaccination. Therefore, understanding the preferences of TCM clinicians towards influenza vaccines is of great importance. To understand preferences for features of influenza vaccination programs and identify the optimal influenza vaccination program among clinicians practicing TCM and Western medicine. We conducted a discrete choice experiment with a national sample of 3085 Chinese clinicians from various hospital levels (n=1013 practicing TCM) from January to May 2022. Simulations from choice models using the experimental data generated the coefficients of preference and predicted the uptake rate of different influenza vaccination programs. Clinicians were grouped by vaccine preference classification through a latent class analysis. All included attributes significantly influenced clinicians' preferences for choosing an influenza vaccination program. An approximate hypothetical 60% increase of vaccine uptake could be obtained when the attitude of the workplace changed from "no notice" to "encouraging of vaccination"; there was an approximate hypothetical 35% increase of vaccine uptake when vaccination campaign strategies changed from "individual appointment" to "vaccination in a workplace setting." In the entire sample, about 30% (946/3085) of clinicians preferred free vaccinations, while 26.5% (819/3085) comprehensively considered all attributes, except vaccination campaign strategies, when making a decision about choosing an influenza vaccination program. Clinicians who practiced TCM, worked in tertiary hospital, or had at least a postgraduate degree exhibited a lower preference for free vaccinations. Clinicians who practiced Western medicine, worked in primary hospital, or had at most a bachelor's degree had a higher preference for vaccinations in workplace settings. Offering a range of influenza vaccination programs targeting the preferred attributes of different clinician groups could potentially encourage more clinicians, including those practicing TCM, to participate in influenza vaccination programs.

Consumer preference study for the interface design of traditional Chinese medicine applications using conjoint analysis method

The widespread use of mobile applications (apps) offers a new platform for sustaining traditional culture, yet insufficient focus on interface design has hindered user experience. This paper focuses on traditional Chinese medicine (TCM) apps, examining user preferences for interface design elements and their combinations across four dimensions: visual effects, functional attributes, layout, and interaction modes. Utilizing Conjoint Analysis Method (CAM), this study quantitatively explores user preferences for the combination schemes of 18 orthogonal designs. The study collected 464 users’ data through questionnaires and processed the data using CAM and Multivariate Analysis of Variance (MANOVA). The findings indicate that users have a high demand for theme colors (35.09%), text styles (27.32%), and icon styles (14.78%) in medical consultation apps. For health education apps, users exhibit preferences for text styles (24.95%), theme colors (24.58%), and functional architecture (16.30%). In professional learning apps, the preferred elements are theme colors (24.07%), text styles (18.54%), and icon styles (18.03%). Based on these results, the paper proposes corresponding design strategies for each type of TCM app. This study reveals the characteristics of user preferences for different categories of TCM apps, enriching the theories and methods of interaction design for similar mobile applications. Ultimately, this facilitates the integration of traditional Chinese medicine culture with modern digital technology, promoting the digital and sustainable transmission of traditional culture.

Understanding Daily Care Experience Preferences Across the Lifespan of Older Adults: Application of Natural Language Processing.

Older adults are a heterogeneous group, and their care experience preferences are likely to be diverse and individualized. Thus, the aim of this study was to identify categories of older adults' care experience preferences and to examine similarities and differences across different age groups. The initial categories of older adults' care experience preferences were identified through a qualitative review of narrative text (n = 3134) in the ADVault data set. A natural language processing (NLP) algorithm was used to automatically and accurately define older adults' care experience preference categories. Descriptive statistics were used to examine similarities and differences in care experience preference categories across different age groups. The overall average performance of NLP algorithms was relatively high (average F-score = 0.88; range: 0.77-0.96). Through a qualitative review of 350 randomly selected texts, a total of 11 categories were identified. The most frequent category was music, followed by photographs, entertainment, family/friends, religion-related, atmosphere, flower/plants, pet, bed/bedding, hobby, and other. After applying the best performing NLP algorithm to each category, older adults' care experience preference categories were music (41.32%), followed by photographs (28.47%), entertainment (13.46%), religion-related (n = 12.69%), pet (12.22%), flower/plants (11.51%), family/friends (8.45%), atmosphere (7.75%), bed/bedding (6.12%), and hobby (5.45%). Young-old and old-old adults had similar care experience preferences with music being the most frequent category while old-old adults had photographs as the most frequent category for their care experience preference. Clinicians must understand the distinct categories of care experience preferences and incorporate them into personalized care planning.

Postmarketing Safety Surveillance of Topiramate: A Signal Detection and Analysis Study Based on the FDA Adverse Event Reporting System Database.

This study aims to investigate the occurrence of adverse events associated with topiramate by analyzing data from the FDA Adverse Event Reporting System. The goal is to provide a basis for the safe clinical use of topiramate. Adverse event data from the FDA Adverse Event Reporting System, from its inception through the first quarter of 2024, were extracted. Signal detection was conducted using three methods: the reporting odds ratio, the medicines and healthcare products regulatory agency method, and the Bayesian confidence propagation neural network. Adverse events were statistically analyzed according to the preferred term and system organ class classifications from the Medical Dictionary for Regulatory Activities version 27.0. Positive signals were then compared against the drug label and the Important Medical Event list. A total of 12,168 adverse event reports involving topiramate as the primary suspect were analyzed, resulting in the extraction of 244 positive signals across 15 system organ classes. Among these, 21 signals were identified as serious adverse reactions not included in the drug label, encompassing 5 system organ classes. Notable signals included hypospadias, spina bifida, abortion spontaneous, renal tubular dysfunction, uveitis, retinal detachment, and choroidal effusion. Additionally, signals such as osmotic demyelination syndrome and Arnold-Chiari malformation were identified as requiring further monitoring. This study identified several unexpected and serious adverse reaction signals that align with previously reported cases. These findings underscore the need for ongoing study, focused attention, and vigilant monitoring during the clinical use of topiramate.

Enhancing environmental sustainability through code-driven process integration in the petrochemical industry

Balancing various objectives and navigating uncertainties, reducing CO2 emissions and enhancing energy efficiency in industry presents a complex challenge. While previous studies primarily focused on conventional optimization methods, this research introduces an innovative approach: a multi-criteria optimization framework tailored to address uncertainties. The primary objective is to optimize energy consumption, minimize emissions, and improve cost efficiency simultaneously within the petrochemical industry. To effectively manage uncertain variables, this study integrates decision-making simulations and expert insights through a hybrid methodology to yield optimal outcomes. Employing three distinct preference categories, the model formulates comprehensive decision-making strategies. Empirical findings underscore the model’s efficacy in reducing CO2 emissions, bridging crucial gaps in existing research, and advocating sustainable practices in the sector. Departing from conventional methodologies, this research leverages advanced decision-making techniques adept at handling uncertainty. The framework identifies pivotal emission sources and advocates economically viable reduction strategies. Its adaptability enriches our comprehension of emission challenges by considering diverse factors and expert perspectives. Professional assessments affirm the model’s success and propose a Coding-Based Prototype as a strategic tool for addressing uncertainties. These results underscore the imperative for policy reforms, such as embracing carbon capture technologies, to bolster global sustainability and foster enduring growth in the industrial domain.

Patient Preferences for Features Associated With Leadless Versus Conventional Transvenous Cardiac Pacemakers.

Regulatory approval of the first dual-chamber leadless pacemaker system provides patients an alternative to conventional transvenous pacemakers. The study objective was to quantify the preferences of patients for pacemaker features. Patients with a de novo (ie, initial) pacemaker indication were recruited from 7 US sites to complete a Web-based discrete-choice experiment survey between May 11, 2022, and May 24, 2023. Patients chose between pairs of experimentally designed, hypothetical pacemakers that varied according to type (removable leadless, nonremovable leadless, or conventional transvenous); battery life (5, 8, 12, or 15 years); time since regulatory approval (2 or 10 years); discomfort for 6 months (none or discomfort); and complication risk and infection risk (1%, 5%, or 10%/20% for each). Patients' choice data were analyzed using random-parameter logit models and latent-class analysis. Among 117 participants, the mean (SD) age was 67.3 (14.6) years, 94% were white, and 42% were female. On average, patients' survey responses revealed a preference for removable leadless pacemakers (β, 0.340; SE, 0.096) over both nonremovable leadless pacemakers (β, -0.310; SE, 0.131; P=0.001) and conventional transvenous pacemakers (β, -0.030; SE, 0.119; P=0.031). However, latent-class analysis revealed 2 distinct preference classes. One class preferred leadless pacemakers (50.5%), and the other class preferred conventional transvenous pacemakers (49.5%). The conventional pacemaker class prioritized pacemakers with 10 rather than 2 years since regulatory approval (P<0.001), whereas the leadless pacemaker class was insensitive to years since regulatory approval (P=0.83). Complication risks and infection risks were found to be the most influential. All else equal, patients would accept maximum risks of complications or infections ranging about 5% to 18% to receive their preferred pacemaker type. Latent-class analysis revealed strong patient preferences for the type of pacemaker, with a nearly equal split between recent leadless pacemaker technology and conventional transvenous pacemakers. These findings can inform shared decision-making between health care providers and patients. URL: https://www.clinicaltrials.gov; Unique identifier: NCT05327101.

Informativeness orders over ambiguous experiments

We generalize Blackwell's informativeness order to ambiguous experiments. A decision maker might view a statistical experiment as ambiguous if she faces uncertainty about the data generating process for its signals. Formally, an ambiguous experiment is modeled as a mapping from an auxiliary state space to the set of unambiguous experiments. Each auxiliary state corresponds to a possible data generating process. We show that one ambiguous experiment is preferred to another by every decision maker in every decision problem if and only if they are related by a condition called prior-by-prior dominance, which states that for any first-order belief the decision maker entertains on the auxiliary state space, the expected experiment resulting from this belief for the first experiment is Blackwell more informative than that of the second. This equivalence is robust for any class of monotone ambiguity preferences that nests expected utility. We obtain another informativeness order when we restrict attention to decision makers who apply the maxmin criterion to evaluate ambiguous experiments and connect this informativeness order to comparisons of sets of Blackwell experiments.

RADT-13. REAL-WORLD DATA ON NEWLY DIAGNOSED WHO 4 GLIOMAS UNDERGOING TTFIELDS: SURVIVAL ANALYSIS AND RELAPSE PATTERNS FROM A SINGLE CENTER

Abstract BACKGROUND Tumor treating fields (TTFields) therapy is more and more widely used to treat patients with glioblastoma due to its effectiveness and low toxicity. In the 2023 V1 NCCN guidelines, TMZ combined with TTFields is the only preferred Class 1 recommended standard treatment for ndGBM patients aged ≤70 years. METHOD A total of 37 patients who were newly diagnosed as WHO 4 Gliomas in Tian Tan Hospital from 2020.8 to 2023.10 with TTFields were included in this retrospective analysis, and the median follow-up time was 449 days. RESULT The median age of the 37 patients was 50 years old, and 17 of them were male. Total resection was performed in 18 patients (48.6 %) and nearly total resection was performed in 14 patients (37.8%). The median time from diagnosis to first wearing of TTFields was 83 days(2.76 months). The median wearing time of TTFields was 9 months, and the median wearing compliance was 90%.The median PFS from diagnosis was 494 days (16.5 months). The 18 months OS rate was 86.7% from diagnosis to time of death after wearing TTFields. Among 37 patients receiving TTFields, 6 (16.2%) were found to have radiation damage. A total of 3 patients had non-radiological area and distal contralateral recurrence, and PFS were 7.2 months, 16.1 months, and 22.8 months, respectively. CONCLUSION PFS is associated with high total resection rate, early wearing of TTFields (median 2.76 months), and long wearing (median 9 months) with high compliance (median 90%). The recurrence pattern of patients with long-term wearing TTFields needs to be further explored. Currently, we are integrating clinical information and exploring the imaging characteristics of patients with distant metastases based on multimodality radiomics platform.

Investigating the Safety Profile of Fast-Track COVID-19 Drugs Using the FDA Adverse Event Reporting System Database: A Comparative Observational Study.

The US Food and Drug Administration (US FDA) granted emergency use authorization (EUA) for multiple coronavirus disease 2019 (COVID-19) drugs as a medical countermeasure during the COVID-19 pandemic. Despite these drugs' fast-track nature, concerns persist regarding their efficacy and potential adverse effects. Thus, the continuous surveillance and understanding of these drugs' safety profiles are crucial in such scenarios. Using the FDA Adverse Event Reporting System (FAERS) database, we aimed to compare the adverse drug reactions (ADRs) of four fast-track COVID-19 drugs to explore the potential of real-world data for providing prompt feedback in clinical settings. To evaluate the post-marketing safety of fast-track COVID-19 drugs, we descriptively evaluated the ADRs of four COVID-19 drugs (bebtelovimab, molnupiravir, nirmatrelvir/ritonavir, and remdesivir) using FAERS data reported from January 2020 to June 2022. We examined FAERS case records of COVID-19 drugs reported as the "primary suspect drug" as a case group and the records of other drugs as the control. "Serious adverse drug reactions (SADRs)" were defined based on FDA guidelines. Using reporting odds ratios, disproportionality analysis was conducted to determine significant signals for ADRs related to each of the four drugs compared with those of others, both at the preferred term (PT) and system organ class (SOC) levels. To explore the occurrence of reporting each serious outcome reported to the four drugs, we fitted logistic regression models, adjusting for age and sex. During the study period, 5 248 221 cases were submitted to FAERS, including 17 275 cases of the four COVID-19 drugs: bebtelovimab (532 cases), molnupiravir (1106 cases), nirmatrelvir/ritonavir (9217 cases), and remdesivir (6420 cases). A total of 64, 46, 116, and 207 PTs with significant disproportionality were identified for each drug, respectively. "Infusion-related reaction" (18.4%), "diarrhea" (7.4%), "dysgeusia" (11.4%), and "increased alanine aminotransferase" (14.5%) were the most frequently reported SADRs for bebtelovimab, molnupiravir, nirmatrelvir/ritonavir, and remdesivir, respectively. Among the 27 SOCs, statistically significant signals were observed in 10, 3, 0, and 8 SOCs for bebtelovimab, molnupiravir, nirmatrelvir/ritonavir, and remdesivir, respectively. Remdesivir showed a higher occurrence for the reporting of death or life-threatening ADRs compared with the control (adjusted odds ratio (OR) = 2.44, 95% confidence interval (CI) = 2.23-2.59; adjusted OR = 1.82, 95% CI = 1.64-2.02, respectively). We identified potential ADRs associated with COVID-19 drugs and provided insights into their real-world safety. This study demonstrated that real-world data and real-time safety reviews could be effective methods for the timely detection of ADR signals of drugs that have received fast-track approval, as exemplified by COVID-19 drugs. These findings underscore the importance of the continued surveillance, efficient data processing, and establishment of automated pipelines for real-time safety reviews.

A comprehensive framework of visual elements in brand logos: content analysis of top 500 global brands

PurposeDesigners must recognise the significance of a brand logo’s visual elements as they convey various meanings. While studies have attempted to collate visual elements, efforts have often been limited to specific types of visual elements (e.g. typefaces) or restricted to certain product categories. This study aims to conceptualise a comprehensive list of visual elements used in brand logos and to validate it based on the top 500 global brand logos across eight product categories.Design/methodology/approachA comprehensive list was conceptualised through a combination of literature review, interviews with experts and observations of real brand logos. Using this exhaustive compilation, content analysis of the top 500 global brand logos was conducted to discern the prevalent trends of the visual elements in logos across various product categories.FindingsThe content analysis results highlighted an extensive preference for use of wordmark, no outline and horizontal proportion in brand logos. Overall, 42.6% and 31% of brands preferred using cool and neutral colours, respectively. Moreover, the preference for colour categories (warm/cool/neutral), font types (organic/geometric) and stacking of elements (horizontal/vertical/diagonal) varied across product categories.Originality/valueThis study contributes to the literature by creating a comprehensive guidebook of visual elements of logos. It also offers guidance to in-house and third-party designers of companies for logo design to better understand the nuances of intellectual property related to logo elements and assists managers in comparing their brands’ visual identity with those of their competitors.

Investigating aquaculture farmers' preferences for multiobjective green production schemes: A discrete choice experiment in China

In recent years, the objectives of green agricultural development have increasingly diversified, incorporating food safety, pollution control, and resource conservation. This diversification has further heightened the potential for conflicts between policy objectives and the individual interests of farmers. Understanding farmer trade-offs with respect to multiobjective green production (MOGP) policies is crucial for the comprehensive implementation of agricultural green development policies. The green and healthy aquaculture action (GHAA) encompassing multiple green technology objectives promoted by the Chinese government provides a favorable policy context. On the basis of the policy background of the GHAA, this study utilized the choice experiment method (CEM) to explore the preferences of aquaculture farmers for various green technology objectives and supporting measures. The results indicate that aquaculture farmers are willing to make trade-offs among various green technology objectives with the support of specific measures to transform traditional production methods. Farmers have varying preferences for different green technology objectives. Overall, they tend to prefer implementing compound feed utilization but are averse to implementing aquaculture effluent treatment. In terms of support measures, providing technical guidance is more effective than providing aquaculture insurance in incentivizing farmers to participate in MOGP schemes. Further analysis indicates that there are three latent preference classes, which differ in their preferences for green technology objectives and support measures. Age, gender, aquaculture area, risk preferences, and participation in cooperatives explain the probability of preference class membership.

Rationale and design of Dapagliflozin vErsus SacubiTrIl-valsartaN therapY in Heart Failure with reduced ejection fraction (DESTINY-HF): a pragmatic randomised controlled trial protocol

BackgroundHeart failure affects almost 64 million people, with more than half of it constituting heart failure with reduced ejection fraction (HFrEF). Angiotensin receptor-neprilysin inhibitors (ARNI) and sodium-glucose cotransporter-2 (SGLT2) inhibitors...

An evolutionary algorithm‐based classification method for high‐dimensional imbalanced mixed data with missing information

AbstractThe data scale keeps growing by leaps and the majority of it is high‐dimensional imbalanced data, which is hard to classify. Data missing often happens in software which further aggravates the difficulty of classifying the data. In order to resolve high‐dimensional imbalanced mixed‐variables missing data classification problem, a novel method based on particle swarm optimization is developed. It has three original components including multiple feature selection, mixed attribute imputation, and quantum oversampling. Multiple feature selection uses a two‐stage strategy to obtain stable relevant features. Mixed attribute imputation separates features into continuous and discrete features and fills missing values with different models. Quantum oversampling chooses instances to balance data based on the quantum operator. Furthermore, particle swarm optimization is employed to optimize the parameters of the components to obtain preferable classification results. Six representative classification datasets, six typical algorithms, and four measures are taken to conduct exhaust experiments, and results indicate that the proposed method is superior to the comparison algorithms.

Digital learning tools in the classroom: comparing their impacts on student motivation to learn and academic achievement in post-secondary students from Canada, Germany, and India

ABSTRACT Use of digital learning tools (DLTs) in classrooms has markedly increased since the COVID-19 pandemic began. Concerns exist that some DLTs were integrated without careful consideration of their impacts on student motivation to learn and/or academic achievement. Moreover, differences in student demographic profiles and the learning environment may also impact potential relationships. We surveyed post-secondary students from Canada, Germany, and India to determine if DLT use, effectiveness, and/or mode of course delivery differed across jurisdictions, and if any relationships exist between use of different types of DLTs and student GPA. Results indicate that although students from all countries examined preferred classes utilising particular types of DLTs, increased use of DLTs did not improve academic achievement. Nevertheless, it is crucial that DLTs continue to play key roles in modernising our pedagogical approaches given their impacts on course satisfaction and general appeal to the sensibilities of today’s students.

Enhancing MeshNet for 3D shape classification with focal and regularization losses

With the development of deep learning and computer vision, an increasing amount of research has focused on applying deep learning models to the recognition and classification of three-dimensional shapes. In classification tasks, differences in sample quantity, feature amount, model complexity, and other aspects among different categories of 3D model data cause significant variations in classification difficulty. However, simple cross-entropy loss is generally used as the loss function, but it is insufficient to address these differences. In this paper, we used MeshNet as the base model and introduced focal loss as a metric for the loss function. Additionally, to prevent deep learning models from developing a preference for specific categories, we incorporated regularization loss. The combined use of focal loss and regularization loss in optimizing the MeshNet model’s loss function resulted in a classification accuracy of up to 92.46%, representing a 0.20% improvement over the original model’s highest accuracy of 92.26%. Furthermore, the average accuracy over the final 50 epochs remained stable at a higher level of 92.01%, reflecting a 0.71% improvement compared to the original MeshNet model’s 91.30%. These results indicate that our method performs better in 3D shape classification task.

DRGNN: Disentangled representation graph neural network for diverse category-level recommendations

Graph neural networks (GNNs) have significantly advanced recommender systems (RecSys) by enhancing their accuracy in complex collaborative filtering scenarios. However, this progress often comes at the cost of overlooking the diversity of recommendations, a factor in user satisfaction. Addressing this gap, this paper introduces the disentangled representation graph neural network (DRGNN). DRGNN integrates diversification into the candidate generation stage using two specialized modules. The first employs disentangled representation learning to separate item preferences from category preferences, thereby mitigating category bias in recommendations. The second module, focusing on positive sample selection, further reduces category bias. This approach not only maintains the high-order connectivity strengths of GNNs but also substantially improves the diversity of recommendations. Our extensive validation of DRGNN on three comprehensive web service datasets, Taobao, Amazon Beauty and MSD, shows that it not only matches the state-of-the-art methods in accuracy but also excels in achieving a balanced trade-off between accuracy and diversity in recommendations.

Utility-implied term structures of equity risk premia

The fundamental asset pricing problem is extended to the frequency domain for commonly used utility functional forms. For each class of utility functions, closed-form solutions are presented for the term structure of the equity risk premium. The term structure is flat for time separable one-period utility and uncorrelated external habit formation utility, downward sloping for internal habit formation utility, upward sloping for time preference (recursive) utility, and can be upward sloping, downward sloping, or flat for production-based utility. These results suggest how observed risk premium term structures can be mapped to linear time series models consistent with utility preference classes.

Signaling under double-crossing preferences: The case of discrete types

The class of double-crossing preferences, where signaling is cheaper for higher types than for lower types at low signaling levels and the opposite is true at high signaling levels, underlines the phenomenon of countersignaling, with intermediate types choosing higher actions than higher and lower types. We provide an algorithm to systematically construct an equilibrium and thus establish equilibrium existence for this general class of preferences with an arbitrary discrete-type distribution. Our analysis sheds light on the connection between discrete-type and continuous-type models and clarifies robust predictions of signaling under double-crossing preferences.

Four Temporalities: Toward a Typology of Narrative Forms

I outline four temporalities that appear in highly regarded explanatory historical social science. Given William Sewell’s centrality to the literature, I do so through a critique of his proposition that there are “three temporalities”—experimental time, teleology, and eventfulness—and that only the last of them is valid. I concede that his rejection of “experimental” time is justified. But I argue that the category of “teleology,” which Sewell rejects, encompasses two forms of transitional change—“tendencies” and “thresholds”—that are coherent and defensible. I further argue that his preferred category of “eventfulness” really refers to two distinct temporalities—“coincidences” and “contrivances”—rather than just one. I illustrate tendencies, thresholds, coincidences, and contrivances in the works of John Veugelers, Ivan Ermakoff, Marshall Sahlins, and, of course, Sewell.