Profile-based Approach Research Articles

Cyclic Ion Mobility of Isomeric New Psychoactive Substances Employing Characteristic Arrival Time Distribution Profiles and Adduct Separation.

Analysis of new psychoactive substances (NPS), which is essential for toxicological and forensic reasons, can be made complicated by the presence of isomers. Ion mobility has been used as a standalone technique or coupled to mass spectrometry to detect and identify NPS. However, isomer separation has so far chiefly relied on chromatography. Here we report on the determination of isomeric ratios using cyclic ion mobility-mass spectrometry without any chromatographic separation. Isomers were distinguished by mobility separation of lithium adducts. Alternatively, we used arrival time distribution (ATD) profiles that were characteristic of individual isomers and were acquired for protonated molecules or fragment ions. Both approaches provided comparable results. Calculations were used to determine the structures and collision cross sections of both protonated and lithiated isomers that accurately characterized their ion mobility properties. The applicability of ATD profiles to isomer differentiation was demonstrated using direct infusion and flow injection analysis with electrospray of solutions, as well as desorption electrospray of solid samples. Data processing was performed by applying multiple linear regression to the ATD profiles. Using the proposed ATD profile-based approach, the relationships between the determined and given content of isomers showed good linearity with coefficients of determination typically greater than 0.99. Flow injection analysis using an autosampler allowed us to rapidly determine isomeric ratios in a sample containing two isomeric pairs with a minor isomer of 10% (determined 9.3% of 3-MMC and 11.0% of 3-FMC in a mixture with buphedrone and 4-FMC). The proposed approach is not only useful for NPS, but also may be applicable to small isomeric molecules analyzed by ion mobility when complete separation of isomers is not achieved.

Fitting in a workgroup in unique ways: A latent profile analysis of perceived person-group fit characteristics.

Research has identified seven characteristics-value congruence, shared interests, perceived demographic similarity, needs-supplies match, goal similarity, common workstyle, and complementary attributes-on which group members simultaneously evaluate their perceived person-group (PG) fit. Most of extant research has focused on how each characteristic or them as a composite predicts outcomes. However, these variable-centered approaches fail to address how there may be subpopulations of members who differentially combine the PG fit characteristics and how such conjunctive effects differentially relate to various work outcomes. To address these issues, we adopt a profile-based approach using latent profile analysis to understand how group members are similar to and different from each other on more holistic configurations of perceived PG fit experiences. With two widely different samples of employees working in group settings, we found seven unique profiles of PG fit: perfect fits, comfortable fits, surface-level misfits, out of syncs, social misfits, lone wolves, and total misfits. We also found in Sample 2 that these profiles differentially predicted group member outcomes commonly studied in the PG fit literature, including attitudes (satisfaction and cohesion), performance behaviors (task performance and citizenship behaviors of helping and voice), and withdrawal (social loafing and turnover). Complementing research that used variable-centered approaches, our profile-based results reveal new theoretical and practical insights of perceived PG fit, suggesting that different group members have distinct configurations of PG fit, and that higher levels of PG fit are not universally positive, and neither is every type of misfit universally negative. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Not all mindfulness is equal: certain facets of mindfulness have important implications for well-being and mental health across the lifespan.

The connections between the five facets of mindfulness, well-being, and mental health across the lifespan have traditionally been investigated using variable-centered approaches. Less research has investigated these relationships from a person-centered, profile-based approach. In this work, we aimed to identify the profiles of mindfulness in a Canadian lifespan sample (14 to 90 years of age) and investigate how these profiles compared on age, well-being, and mental health. An age- and gender-balanced sample of 1,600 participants completed a questionnaire that measured the five facets of mindfulness; life satisfaction; existential well-being; and anxiety, depression, and stress symptoms. A latent profile analysis was conducted. Five profiles based on the five-facet model of mindfulness were identified: high mindfulness, moderate mindfulness, low mindfulness, nonjudgmentally aware, and judgmentally observing. The 3-step approach to profile comparisons was used to assess age, mental health, and well-being differences across the profiles. Those in the high mindfulness and nonjudgmentally aware profiles were generally older, while the judgmentally observing profile contained younger individuals. Those in the high mindfulness and nonjudgmentally aware profiles reported the greatest mental health and well-being. Conversely, those in the low mindfulness and judgmentally observing profiles had worse mental health than the other profiles. The moderate mindfulness profile was situated between these profile groups on age, mental health, and well-being outcomes. This pattern of results has implications for mindfulness-based intervention research and practice to better account for heterogeneity in mindfulness and better support well-being across the lifespan.

Perception of Brazilian Agricultural Sciences Students on Animal Welfare: A Profile-based Approach

ABSTRACT The agricultural professionals are ethically obligated to provide good care for the animals under their care. We analysed Brazilian agricultural science students’ profiles based on their perceptions of animal welfare (AW). The survey included 239 students from agronomy, animal science, and veterinary courses in 44 universities. A factor analysis and a cluster analysis identified four students’ profiles. “The farm animal stewards” group (n= 79) focused their perceptions of AW on basic health and functioning as a basis for meat, wool, egg, and dairy production, while the “the industrial view” group (n= 15), in the profitability and economic factors. “The animal rights position” (n= 76), in the face of conflicting interests (animals vs. owners), perceive that the animal’s interest should prevail and give an equal treatment for all species. ”The balanced” group (n= 69) incorporates concepts from animal and human to explain their perceptions. The students’ perception of AW is multifaceted and influenced by value-based ideas about what is important or desirable for animals and all stakeholders. Scientific fields focusing on AW need to be emphasized within agricultural science curriculum.

A gene expression profile-based approach to screen the occurrence and predisposed host characteristics of drug-induced liver injury: a case study of Psoralea corylifolia Linn.

Drug-induced liver injury (DILI) is one of the most common causes of a drug being withdrawn, and identifying the culprit drugs and the host factors at risk of causing DILI has become a current challenge. Recent studies have found that immune status plays a considerable role in the development of DILI. In this study, DILI-related differentially expressed genes mediated by immunoinflammatory cytokines were obtained from the Gene Expression Omnibus (GEO) database to predict the occurrence of DILI (named the DILI predictive gene set, DILI_PGS), and the predictability of the DILI_PGS was verified using the Connectivity Map (CMap) and LiverTox platforms. The results obtained DILI_PGS from the GEO database could predict 81.25% of liver injury drugs. In addition, the Coexpedia platform was used to predict the DILI_PGS-related characteristics of common host diseases and found that the DILI_PGS mainly involved immune-related diseases and tumor-related diseases. Then, animal models of immune stress (IS) and immunosuppressive (IP) were selected to simulate the immune status of the above diseases. Meanwhile, psoralen, a main component derived from Psoralea corylifolia Linn. with definite hepatotoxicity, was selected as an experimental drug with highly similar molecular fingerprints to three idiosyncratic hepatotoxic drugs (nefazodone, trovafloxacin, and nimesulide) from the same DILI_PGS dataset. The animal experiment results found a single administration of psoralen could significantly induce liver injury in IS mice, while there was no obvious liver function change in IP mice by repeatedly administering the same dose of psoralen, and the potential mechanism of psoralen-induced liver injury in IS mice may be related to regulating the expression of the TNF-related pathway. In conclusion, this study constructed the DILI_PGS with high accuracy to predict the occurrence of DILI and preliminarily identified the characteristics of host factors inducing DILI.

People with opioid use disorders: A taxonomy of treatment entrants to support the development of a profile‐based approach to care

People with opioid use disorders (OUD) present with high levels of medical and psychosocial vulnerabilities. In recent years, studies have highlighted a shift in demographic and biopsychosocial profiles of people with OUD. In order to support the development of a profile-based approach to care, this study aims to identify different profiles of people with OUD in a sample of patients admitted to a specialised opioid agonist treatment (OAT) facility. Twenty-three categorical variables (demographic, clinical, indicators of health and social precariousness) were retrieved from a sample of 296 patient charts in a large Montréal-based OAT facility (2017-2019). Descriptive analyses were followed by a three-step latent class analysis (LCA) to identify different socio-clinical profiles and examine their association with demographic variables. The LCA revealed three socio-clinical profiles: (i) "polysubstance use with psychiatric, physical and social vulnerabilities" (37% of the sample); (ii) "heroin use with vulnerabilities to anxiety and depression" (33%); (iii) "pharmaceutical-type opioid use with vulnerabilities to anxiety, depression and chronic pain" (30%). Class 3 individuals were more likely to be aged 45 years and older. While current approaches (such as low- and regular-threshold services) may be suited for many OUD treatment entrants, there may be a need to improve the continuum of care between mental health, chronic pain, and addiction services for those characterised by the use of pharmaceutical-type opioids, chronic pain and older age. Overall, the results support further exploring profile-based approaches to care, tailored to subgroups of patients with differing needs or abilities.

PLI-Assess: A Behavior Profile-Based Approach for Privacy-Preserving Log Assessment

PLI-Assess: A Behavior Profile-Based Approach for Privacy-Preserving Log Assessment

Education Research: Impact of Burnout on Neurology Residents and Research Fellows in Europe

Background and Objectives To investigate the prevalence of burnout profiles and their contributing factors in European neurology residents, junior neurologists, and research fellows. Methods The members of the Resident and Research Fellow Section (RRFS) of the European Academy of Neurology were surveyed using standardized instruments for burnout, job satisfaction, social support, depression, work-related fatigue, work-life integration, and impact of life events, from September 1, 2020, to January 6, 2021. Results The response rate was 23.1% (332 responders of 1,439 contacted RRFS members); the median age of participants 30 years, with 61.5% being female. Our analysis revealed that 73.9% of the responders showed a perturbation in the Maslach Burnout Inventory dimensions, thus fulfilling the criteria for a dysfunctional phenotype within the burnout spectrum: burnout (22.6%), overextended (26.1%), ineffective (21.0%), and disengaged (4.1%). Multivariate logistic regression analysis indicated that elevated number of night shifts per month, work-related fatigue, and low professional satisfaction are independent risk factors for burnout. Being a certified neurologist, working in an academic environment, and a higher job satisfaction are associated with the engaged profile, a productive state characterized by high energy, strong involvement, and a sense of efficacy. Discussion Burnout experience is highly prevalent among neurology residents, junior neurologists, and research fellows in Europe. The profile-based approach in this survey captures the multifaceted nature of burnout experience, therefore highlighting mitigating strategies that can be tailored to the distinct dysfunctional burnout profile.

A Profile-based Approach to Finding New Water Fountain Candidates using Databases of Circumstellar Maser Sources

AbstractWater fountains (WFs) are thought to represent an early stage in the morphological evolution of circumstellar envelopes surrounding low- and intermediate-mass evolved stars. These objects are considered to transition from spherical to asymmetric shapes. Despite their potential importance in this transformation process of evolved stars, there are only a few known examples. To identify new WF candidates, we used databases of circumstellar OH (1612 MHz) and H2O (22.235 GHz) maser sources, and compared the velocity ranges of the two maser lines. Finally, 41 sources were found to have a velocity range for the H2O maser line that exceeded that of the OH maser line. Excluding known planetary nebulae and after reviewing the maser spectra in the original literature, we found for 11 sources the exceedance as significant, qualifying them as new WF candidates.

The Impact of Cognitive Load on Students’ Academic Writing: An Authorship Verification Investigation

Automatic authorship verification is known to be a challenging machine learning task. In this paper, we examine the efficacy of an enhanced common n-gram profile-based approach to assist educational institutions to validate students' essays and assignments through their writing styles. We investigated the impact that essays with different cognitive load requirements have in students' writing styles, which may or may not impact authorship verification methods. A total of 46 undergraduate students completed six essays in a laboratory study. Although results showed small and mixed effects of the tasks differing in cognitive load on the different writing product metrics, students' essays and assignments texts contained features that remained stable across essays requiring different levels of cognitive load. These results suggest that our approach could be successfully used in authorship verification, potentially helping to address issues related to academic integrity in higher education settings.

What slaughterhouse workers' attitudes and knowledge reveal about human-pig relationships during pre-slaughter operations: A profile-based approach

Slaughterhouse workers are strategic capital for the meat industry in terms of operational and animal welfare issues; however, information about the attitudes of workers toward the human-animal relationship is limited. The main aim of our study was to identify the profiles of workers based on their attitudes toward pigs, occupational satisfaction, sociodemographics, and animal handling. The survey included 171 workers in 12 Colombian pig slaughterhouses. A factor analysis and a hierarchical cluster analysis identified four segments or worker profiles. The first comprised workers who relate to animals and their work in a mechanical way, the second comprised professional workers who are emotionally close to animals, the third comprised those committed to animals and their work, and the fourth comprised workers who are apathetic toward animals and work activity. The human-animal relationship at the slaughterhouse level is multifaceted, but is influenced by dependent on work satisfaction and sympathy toward the animals.

An arc profile-based approach to evaluate gas pollutants in welding.

Welding is widely used to make assembly of structural components and it will trigger serious environmental pollution, especially waste gas, i.e., carbon dioxide (CO2), ozone (O3), nitrogen oxide (NOx), and particulate matter (PM). It is hard to accurately measure gas pollutants because of their fluidity and diffusivity. However, the pollutants could be evaluated by exploring its generation procedure, i.e., how these pollutants are produced and how to quantify these pollutants. In this paper, an arc profile-based approach to evaluate the emissions of gas pollutants in welding was proposed. The emission of gas pollutants in welding can be calculated according to the chemical reaction and corresponding reaction condition, i.e., the intensity of discharge that determines the coverage volume of the welding arc. To obtain the coverage volume, the welding arc was observed using a high-speed camera and the arc edge was extracted and reconstructed by a binarization processing based method. A welding experiment was performed for recording the arc shape and measuring the emission of gas pollutants. Results show that the measured concentrations of NOx and O3 are 70% and 79% of the calculated emissions of gas pollutants, respectively. It demonstrates the proposed method is credible and feasible, which can help quantitatively analyze the emission of gas pollutants. Meanwhile, the influence of welding time, welding current, and arc length on the emission of gas pollutants was investigated for lowering emission of gas pollution in welding, in order to support the development of sustainable manufacturing processes.

Ambulatory Assessment in Concussion Clinical Care and Rehabilitation.

Concussion is a mild traumatic brain injury that is characterized by a wide range of physical, emotional, and cognitive symptoms as well as neurocognitive, vestibular, and ocular impairments that can negatively affect daily functioning and quality of life. Clinical consensus statements recommend a targeted, clinical profile-based approach for management and treatment. This approach requires that clinicians utilize information obtained via a clinical interview and a multi-domain assessment battery to identify clinical profile(s) (e.g., vestibular, mood/anxiety, ocular, migraine, cognitive fatigue) and prescribe a corresponding treatment/rehabilitation program. Despite this comprehensive approach, the clinical picture can be limited by the accuracy and specificity of patient reports (which often conflate timing and severity of symptomology), as well as frequency and duration of exposure to symptom exacerbating environments (e.g., busy hallways, sitting in the back seat of a car). Given that modern rehabilitation programs leverage the natural environment as a tool to promote recovery (e.g., expose-recover approach), accurate characterization of the patient clinical profile is essential to improving recovery outcomes. Ambulatory assessment methodology could greatly benefit concussion clinical care by providing a window into the symptoms and impairments experienced by patients over the course of their daily lives. Moreover, by evaluating the timing, onset, and severity of symptoms and impairments in response to changes in a patient's natural environment, ambulatory assessments can provide clinicians with a tool to confirm clinical profiles and gauge effectiveness of the rehabilitation program. In this perspective report, we review the motivations for utilizing ambulatory assessment methodology in concussion clinical care and report on data from a pilot project utilizing smart phone-based, ambulatory assessments to capture patient reports of symptom severity, environmental exposures, and performance-based assessments of cognition for 7 days following their initial evaluation.

Sequencing of Pharmacotherapy for Heart Failure with Reduced Ejection Fraction: A Clinical Profile-Based Approach

A Brief History of Heart Failure (HF) Treatment Until 1987, HF treatment consisted in: None of these recommendations was based on clinical or epidemiological evidence, instead relying on the pathophysiological knowledge of the disease at the time, the pharmacological effect of medications, and good sense. […] Sequencing of Pharmacotherapy for Heart Failure with Reduced Ejection Fraction: A Clinical Profile-Based Approach

A Profile-Based Approach to Understanding Social Exchange: Authentic Tour-Guiding in the Sharing Economy

Authenticity in travel and tourism research is often explored at the tangible-existential nexus. However, the collaborative forces embedded within digital sharing economy platforms draw attention to how the promise of social exchange can stimulate authentic consumption. The role of tour guides has evolved accordingly, from professional designation to self-acquired identity enacted ad-hoc. This study therefore applies a deductive-inductive approach—investigating over 100,000 tour guide profiles on the digital sharing economy platform showaround.com —to frame authenticity as the diverse manifestation of promised social exchanges. Combining automated qualitative analysis for large datasets with manual inductive reasoning, the findings identify four types of authenticity demonstrated within sharing economy tour guide profiles: situational, natural, personal, and positional. Doing so extends established understanding of tangible-existential and sincere-trustworthy tourism industry authenticity by capturing social exchanges in dynamic contexts; acknowledging the diverse nature of the promise of authentic tourism service delivery and consumption therein.

A profile-based sentiment-aware approach for depression detection in social media

Depression is a severe mental health problem. Due to its relevance, the development of computational tools for its detection has attracted increasing attention in recent years. In this context, several research works have addressed the problem using word-based approaches (e.g., a bag of words). This type of representation has shown to be useful, indicating that words act as linguistic markers of depression. However, we believe that in addition to words, their contexts contain implicitly valuable information that could be inferred and exploited to enhance the detection of signs of depression. Specifically, we explore the use of user’s characteristics and the expressed sentiments in the messages as context insights. The main idea is that the words’ discriminative value depends on the characteristics of the person who is writing and on the polarity of the messages where they occur. Hence, this paper introduces a new approach based on specializing the framework of classification to profiles of users (e.g., males or women) and considering the sentiments expressed in the messages through a new text representation that captures their polarity (e.g., positive or negative). The proposed approach was evaluated on benchmark datasets from social media; the results achieved are encouraging, since they outperform those of state-of-the-art corresponding to computationally more expensive methods.

Internet of Samples: Progress report

Material samples form an important portion of the data infrastructure for many disciplines. Here, a material sample is a physical object, representative of some physical thing, on which observations can be made. Material samples may be collected for one project initially, but can also be valuable resources for other studies in other disciplines. Collecting and curating material samples can be a costly process. Integrating institutionally managed sample collections, along with those sitting in individual offices or labs, is necessary to faciliate large-scale evidence-based scientific research. Many have recognized the problems and are working to make data related to material samples FAIR: findable, accessible, interoperable, and reusable. The Internet of Samples (i.e., iSamples) is one of these projects. iSamples was funded by the United States National Science Foundation in 2020 with the following aims: enable previously impossible connections between diverse and disparate sample-based observations; support existing research programs and facilities that collect and manage diverse sample types; facilitate new interdisciplinary collaborations; and provide an efficient solution for FAIR samples, avoiding duplicate efforts in different domains (Davies et al. 2021) enable previously impossible connections between diverse and disparate sample-based observations; support existing research programs and facilities that collect and manage diverse sample types; facilitate new interdisciplinary collaborations; and provide an efficient solution for FAIR samples, avoiding duplicate efforts in different domains (Davies et al. 2021) The initial sample collections that will make up the internet of samples include those from the System for Earth Sample Registration (SESAR), Open Context, the Genomic Observatories Meta-Database (GEOME), and Smithsonian Institution Museum of Natural History (NMNH), representing the disciplines of geoscience, archaeology/anthropology, and biology. To achieve these aims, the proposed iSamples infrastructure (Fig. 1) has two key components: iSamples in a Box (iSB) and iSamples Central (iSC). The iSC component will be a permanent Internet service that preserves, indexes, and provides access to sample metadata aggregated from iSBs. It will also ensure that persistent identifiers and sample descriptions assigned and used by individual iSBs are synchronized with the records in iSC and with identifier authorities like International Geo Sample Number (IGSN) or Archival Resource Key (ARK). The iSBs create and maintain identifiers and metadata for their respective collection of samples. While providing access to the samples held locally, an iSB also allows iSC to harvest its metadata records. The metadata modeling strategy adopted by the iSamples project is a metadata profile-based approach, where core metadata fields that are applicable to all samples, form the core metadata schema for iSamples. Each individual participating collectionis free to include additional metadata in their records, which will also be harvested by iSC and are discoverable through the iSC user interface or APIs (Application Programming Interfaces), just like the core. In-depth analysis of metadata profiles used by participating collections, including Darwin Core, has resulted in an iSamples core schema currently being tested and refined through use. See the current version of the iSamples core schema. A number of properties require a controlled vocabulary. Controlled vocabularies used by existing records are kept, while new vocabularies are also being developed to support high-level grouping with consistent semantics across collection types. Examples include vocabularies for Context Category, Material Category, and Specimen Type (Table 1). These vocabularies were also developed in a bottom-up manner, based on the terms used in the existing collections. For each vocabulary, a decision tree graph was created to illustrate relations among the terms, and a card sorting exercise was conducted within the project team to collect feedback. Domain experts are invited to take part in this exercise here, here, and here. These terms will be used as upper-level terms to the existing category terms used in the participating collections and hence create connections among individual participating collections. iSample project members are also active in the TDWG Material Sample Task Group and the global consultation on Digital Extended Specimens. Many members of the iSamples project also lead or participate in a sister research coordination network (RCN), Sampling Nature. The goal of this RCN is to develop and refine metadata standards and controlled vocabularies for the iSamples and other projects focusing on material samples. We cordially invite you to participate in the Sampling Nature RCN and help shape the future standards for material samples. Contact Sarah Ramdeen (sramdeen@ideo.columbia.edu) to engage with the RCN.

Matrix Profile-Based Approach to Industrial Sensor Data Analysis Inside RDBMS

Currently, big sensor data arise in a wide spectrum of Industry 4.0, Internet of Things, and Smart City applications. In such subject domains, sensors tend to have a high frequency and produce massive time series in a relatively short time interval. The data collected from the sensors are subject to mining in order to make strategic decisions. In the article, we consider the problem of choosing a Time Series Database Management System (TSDBMS) to provide efficient storing and mining of big sensor data. We overview InfluxDB, OpenTSDB, and TimescaleDB, which are among the most popular state-of-the-art TSDBMSs, and represent different categories of such systems, namely native, add-ons over NoSQL systems, and add-ons over relational DBMSs (RDBMSs), respectively. Our overview shows that, at present, TSDBMSs offer a modest built-in toolset to mine big sensor data. This leads to the use of third-party mining systems and unwanted overhead costs due to exporting data outside a TSDBMS, data conversion, and so on. We propose an approach to managing and mining sensor data inside RDBMSs that exploits the Matrix Profile concept. A Matrix Profile is a data structure that annotates a time series through the index of and the distance to the nearest neighbor of each subsequence of the time series and serves as a basis to discover motifs, anomalies, and other time-series data mining primitives. This approach is implemented as a PostgreSQL extension that allows an application programmer both to compute matrix profiles and mining primitives and to represent them as relational tables. Experimental case studies show that our approach surpasses the above-mentioned out-of-TSDBMS competitors in terms of performance since it assumes that sensor data are mined inside a TSDBMS at no significant overhead costs.

Burnout in the Pharmaceutical Activity: The Impact of COVID-19

Objectives: Pharmacists are among the healthcare professionals involved in the response to COVID-19 pandemic, maintaining essential services. The study aimed to assess the occurrence of pharmacist’s burnout and determine outcomes for each of its dimensions; identify potentially associated characteristics; and determine profiles and critical limits.Design and Setting: Cross-sectional observational study comprising an electronic survey addressed to Portuguese Pharmaceutical Society members.Participants: From a total of 1362 participants, 91.4% were involved in direct and 7.7 % in non-direct patient care activity.Outcome measures: The statistical analysis, performed with 2-tailed significance set at PResults: Concern for the future (77.2.5%), workload (72.7%), scarcity of resources (62.4%), and loss of control over the activity (54.6%) were the main influencing factors on work-related feelings. Pharmacists who had been trained in COVID-19 were the most confident and showed a higher efficacy/outcome rate. Workload appears only as a predictor of high EE, with great impact on those who worked during the 3-month period more than 50 hours per week and on those who worked almost every weekend. The latter with greater predictive power of high EE (p≤0,001).Conclusions: The new profile-based approach provides an understanding of the burnout experience and distinct relationships with various work-life factors and suggests that each profile reflects a different work-life crisis that would require a unique intervention strategy. Pharmacists who have been involved in direct care activities are at the highest risk of burnout. However, they were able to avoid depersonalisation and cynicism and thereby burnout syndrome.Funding Information: This work was supported by the Portuguese Pharmaceutical Society. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.Declaration of Interests: None.Ethics Approval Statement: The institutional review board of the of the Portuguese Pharmaceutical Society approved the project as exempt and without ethical reservations to its resolution, since (i) the procedures concerning the anonymity of the data and the information provided to the volunteers are foreseen and (ii) the nature of the elements to be collected does not carry the risk of revealing weaknesses unknown to the volunteer and thus triggering unpredictable reactions. Selecting the electronic link served as first consent to participate voluntarily in the study.

Political New Sincerity and Profilicity

The past few years have seen a dramatic backlash against identity politics from academics such as Michael Sandel, Kwame Appiah, Mark Lilla, and Francis Fukuyama. In the vocabulary of identity conceptions, we can classify this as a reaction to a growing dissatisfaction with the perceived hollowness and ineffectiveness of “authenticity” that calls for a return to “sincerity”—or a “Political New Sincerity.” We argue that a third identity paradigm is in play as well, namely “profilicity.” This profile-based approach to understanding oneself, others, and the world has had a major impact on social and political life, and yet has gone largely unnoticed or otherwise been mis-diagnosed. Our analysis provides a critical reflection on the emergence of profilicity to pave the way for developing insights into our changing sociopolitical and inter-personal landscapes.