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Research on meaningful work has not embraced a shared definition of what it is, in part because many researchers and laypersons agree that it means different things to different people. However, subjective and social accounts of meaningful work have limited practical value to help people pursue it and to help scholars study it. The account of meaningful work advanced in this paper is inherently normative. It recognizes the relevance of subjective experience and social agreement to appraisals of meaningfulness but considers them conceptually incomplete and practically limited. According to this normative account, meaningful work should be meaningful to oneself and to others and is also meaningful independent of them. It sets forth grounds for evaluating some work to be more meaningful than other work, asserting the possibility that one could be mistaken about the meaningfulness of one's work. While it thus proscribes some claims to meaningful work, it also opens up potential new avenues of inquiry into, among other things, self-aggrandizing and harmful work that is experienced as meaningful, morally valuable work that is not experienced as meaningful, and the distinction between experienced and normative meaningfulness.

COVID-19 was first discovered in December 2019 and has since evolved into a pandemic. To address this global health crisis, artificial intelligence (AI) has been deployed at various levels of the health care system. However, AI has both potential benefits and limitations. We therefore conducted a review of AI applications for COVID-19. We performed an extensive search of the PubMed and EMBASE databases for COVID-19-related English-language studies published between December 1, 2019, and March 31, 2020. We supplemented the database search with reference list checks. A thematic analysis and narrative review of AI applications for COVID-19 was conducted. In total, 11 papers were included for review. AI was applied to COVID-19 in four areas: diagnosis, public health, clinical decision making, and therapeutics. We identified several limitations including insufficient data, omission of multimodal methods of AI-based assessment, delay in realization of benefits, poor internal/external validation, inability to be used by laypersons, inability to be used in resource-poor settings, presence of ethical pitfalls, and presence of legal barriers. AI could potentially be explored in four other areas: surveillance, combination with big data, operation of other core clinical services, and management of patients with COVID-19. In view of the continuing increase in the number of cases, and given that multiple waves of infections may occur, there is a need for effective methods to help control the COVID-19 pandemic. Despite its shortcomings, AI holds the potential to greatly augment existing human efforts, which may otherwise be overwhelmed by high patient numbers.

Symptom checkers are digital tools assisting laypersons in self-assessing the urgency and potential causes of their medical complaints. They are widely used but face concerns from both patients and health care professionals, especially regarding their accuracy. A 2015 landmark study substantiated these concerns using case vignettes to demonstrate that symptom checkers commonly err in their triage assessment. This study aims to revisit the landmark index study to investigate whether and how symptom checkers' capabilities have evolved since 2015 and how they currently compare with laypersons' stand-alone triage appraisal. In early 2020, we searched for smartphone and web-based applications providing triage advice. We evaluated these apps on the same 45 case vignettes as the index study. Using descriptive statistics, we compared our findings with those of the index study and with publicly available data on laypersons' triage capability. We retrieved 22 symptom checkers providing triage advice. The median triage accuracy in 2020 (55.8%, IQR 15.1%) was close to that in 2015 (59.1%, IQR 15.5%). The apps in 2020 were less risk averse (odds 1.11:1, the ratio of overtriage errors to undertriage errors) than those in 2015 (odds 2.82:1), missing >40% of emergencies. Few apps outperformed laypersons in either deciding whether emergency care was required or whether self-care was sufficient. No apps outperformed the laypersons on both decisions. Triage performance of symptom checkers has, on average, not improved over the course of 5 years. It decreased in 2 use cases (advice on when emergency care is required and when no health care is needed for the moment). However, triage capability varies widely within the sample of symptom checkers. Whether it is beneficial to seek advice from symptom checkers depends on the app chosen and on the specific question to be answered. Future research should develop resources (eg, case vignette repositories) to audit the capabilities of symptom checkers continuously and independently and provide guidance on when and to whom they should be recommended.

Studies have shown that patients have difficulty understanding medical jargon in electronic health record (EHR) notes, particularly patients with low health literacy. In creating the NoteAid dictionary of medical jargon for patients, a panel of medical experts selected terms they perceived as needing definitions for patients. This study aims to determine whether experts and laypeople agree on what constitutes medical jargon. Using an observational study design, we compared the ability of medical experts and laypeople to identify medical jargon in EHR notes. The laypeople were recruited from Amazon Mechanical Turk. Participants were shown 20 sentences from EHR notes, which contained 325 potential jargon terms as identified by the medical experts. We collected demographic information about the laypeople's age, sex, race or ethnicity, education, native language, and health literacy. Health literacy was measured with the Single Item Literacy Screener. Our evaluation metrics were the proportion of terms rated as jargon, sensitivity, specificity, Fleiss κ for agreement among medical experts and among laypeople, and the Kendall rank correlation statistic between the medical experts and laypeople. We performed subgroup analyses by layperson characteristics. We fit a beta regression model with a logit link to examine the association between layperson characteristics and whether a term was classified as jargon. The average proportion of terms identified as jargon by the medical experts was 59% (1150/1950, 95% CI 56.1%-61.8%), and the average proportion of terms identified as jargon by the laypeople overall was 25.6% (22,480/87,750, 95% CI 25%-26.2%). There was good agreement among medical experts (Fleiss κ=0.781, 95% CI 0.753-0.809) and fair agreement among laypeople (Fleiss κ=0.590, 95% CI 0.589-0.591). The beta regression model had a pseudo-R of 0.071, indicating that demographic characteristics explained very little of the variability in the proportion of terms identified as jargon by laypeople. Using laypeople's identification of jargon as the gold standard, the medical experts had high sensitivity (91.7%, 95% CI 90.1%-93.3%) and specificity (88.2%, 95% CI 86%-90.5%) in identifying jargon terms. To ensure coverage of possible jargon terms, the medical experts were loose in selecting terms for inclusion. Fair agreement among laypersons shows that this is needed, as there is a variety of opinions among laypersons about what is considered jargon. We showed that medical experts could accurately identify jargon terms for annotation that would be useful for laypeople.

Postmortem computed tomography (PMCT) is a standard image modality used in forensic death investigations. Case- and audience-specific visualizations are vital for identifying relevant findings and communicating them appropriately. Different data types and visualization methods exist in 2D and 3D, and all of these types have specific applications. 2D visualizations are more suited for the radiological assessment of PMCT data because they allow the depiction of subtle details. 3D visualizations are better suited for creating visualizations for medical laypersons, such as state attorneys, because they maintain the anatomical context. Visualizations can be refined by using additional techniques, such as annotation or layering. Specialized methods such as 3D printing and virtual and augmented reality often require data conversion. The resulting data can also be used to combine PMCT data with other 3D data such as crime scene laser scans to create crime scene reconstructions. Knowledge of these techniques is essential for the successful handling of PMCT data in a forensic setting. In this review, we present an overview of current visualization techniques for PMCT.

In an effort to address the opioid epidemic, a majority of states recently passed some version of a Good Samaritan law (GSL) and/or a naloxone access law (NAL). Good Samaritan laws provide immunity from prosecution for drug possession to anyone who seeks medical assistance in the event of a drug overdose; NALs allow laypersons to administer naloxone, which temporarily counteracts the effects of an opioid overdose. Using data from the National Vital Statistics System multiple-cause-of-death mortality files for 1999–2014, this study is the first to examine the effects of these laws on overdose deaths involving opioids. The estimated effects of GSLs on opioid-related mortality are consistently negative but not statistically significant. Adoption of an NAL is associated with a statistically significant 9–10 percent reduction in opioid-related mortality, although the negative association between NALs and opioid-related mortality appears to be driven by early adopters—states that passed legislation before 2011.

Health recommender systems (HRSs) offer the potential to motivate and engage users to change their behavior by sharing better choices and actionable knowledge based on observed user behavior. We aim to review HRSs targeting nonmedical professionals (laypersons) to better understand the current state of the art and identify both the main trends and the gaps with respect to current implementations. We conducted a systematic literature review according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and synthesized the results. A total of 73 published studies that reported both an implementation and evaluation of an HRS targeted to laypersons were included and analyzed in this review. Recommended items were classified into four major categories: lifestyle, nutrition, general health care information, and specific health conditions. The majority of HRSs use hybrid recommendation algorithms. Evaluations of HRSs vary greatly; half of the studies only evaluated the algorithm with various metrics, whereas others performed full-scale randomized controlled trials or conducted in-the-wild studies to evaluate the impact of HRSs, thereby showing that the field is slowly maturing. On the basis of our review, we derived five reporting guidelines that can serve as a reference frame for future HRS studies. HRS studies should clarify who the target user is and to whom the recommendations apply, what is recommended and how the recommendations are presented to the user, where the data set can be found, what algorithms were used to calculate the recommendations, and what evaluation protocol was used. There is significant opportunity for an HRS to inform and guide health actions. Through this review, we promote the discussion of ways to augment HRS research by recommending a reference frame with five design guidelines.

Proponents of “enhanced interrogation techniques” in the United States have claimed that such methods are necessary for obtaining information from uncooperative terrorism subjects. In the present article, we offer an informed, academic perspective on such claims. Psychological theory and research shows that harsh interrogation methods are ineffective. First, they are likely to increase resistance by the subject rather than facilitate cooperation. Second, the threatening and adversarial nature of harsh interrogation is often inimical to the goal of facilitating the retrieval of information from memory and therefore reduces the likelihood that a subject will provide reports that are extensive, detailed, and accurate. Third, harsh interrogation methods make lie detection difficult. Analyzing speech content and eliciting verifiable details are the most reliable cues to assessing credibility; however, to elicit such cues subjects must be encouraged to provide extensive narratives, something that does not occur in harsh interrogations. Evidence is accumulating for the effectiveness of rapport-based information-gathering approaches as an alternative to harsh interrogations. Such approaches promote cooperation, enhance recall of relevant and reliable information, and facilitate assessments of credibility. Given the available evidence that torture is ineffective, why might some laypersons, policymakers, and interrogation personnel support the use of torture? We conclude our review by offering a psychological perspective on this important question.

The beauty of science lies within its core assumption that it seeks to identify the truth, and as such, the truth stands alone and does not depend on the person who proclaims it. However, people's proclivity to succumb to various stereotypes is well known, and the scientific world may not be exceptionally immune to the tendency to judge a book by its cover. An interesting example is geographical bias, which includes distorted judgments based on the geographical origin of, inter alia, the given work and not its actual quality or value. Here, we tested whether both laypersons (N = 1532) and scientists (N = 480) are prone to geographical bias when rating scientific projects in one of three scientific fields (i.e., biology, philosophy, or psychology). We found that all participants favored more biological projects from the USA than China; in particular, expert biologists were more willing to grant further funding to Americans. In philosophy, however, laypersons rated Chinese projects as better than projects from the USA. Our findings indicate that geographical biases affect public perception of research and influence the results of grant competitions.

What must laypersons understand about science to allow them to make sound decisions on science-related issues? Relying on recent developments in social epistemology, this article argues that scientific education should have the goal not of bringing laypersons’ understanding of science closer to that of expert insiders but rather of cultivating the kind of competence characteristic of “competent outsiders.” Moreover, it argues that philosophers of science have an important role to play in attempts to promote this kind of understanding but that, to successfully fulfill this role, they will have to approach central questions in the field differently.