Exploring the effects of human-centered AI explanations on trust and reliance

Transparency is widely regarded as crucial for the responsible real-world deployment of artificial intelligence (AI) and is considered an essential prerequisite to establishing trust in AI. There are several approaches to enabling transparency, with one promising attempt being human-centered explanations. However, there is little research into the effectiveness of human-centered explanations on end-users' trust. What complicates the comparison of existing empirical work is that trust is measured in different ways. Some researchers measure subjective trust using questionnaires, while others measure objective trust-related behavior such as reliance. To bridge these gaps, we investigated the effects of two promising human-centered post-hoc explanations, feature importance and counterfactuals, on trust and reliance. We compared these two explanations with a control condition in a decision-making experiment (N = 380). Results showed that human-centered explanations can significantly increase reliance but the type of decision-making (increasing a price vs. decreasing a price) had an even greater influence. This challenges the presumed importance of transparency over other factors in human decision-making involving AI, such as potential heuristics and biases. We conclude that trust does not necessarily equate to reliance and emphasize the importance of appropriate, validated, and agreed-upon metrics to design and evaluate human-centered AI

Family medicine in the Baltic countries

publishersversio

Development and validation of a risk model for prediction of hazardous alcohol consumption in general practice attendees : the PredictAL study

Background: Little is known about the risk of progression to hazardous alcohol use in people currently drinking at safe limits. We aimed to develop a prediction model (predictAL) for the development of hazardous drinking in safe drinkers. Methods: A prospective cohort study of adult general practice attendees in six European countries and Chile followed up over 6 months. We recruited 10,045 attendees between April 2003 to February 2005. 6193 European and 2462 Chilean attendees recorded AUDIT scores below 8 in men and 5 in women at recruitment and were used in modelling risk. 38 risk factors were measured to construct a risk model for the development of hazardous drinking using stepwise logistic regression. The model was corrected for over fitting and tested in an external population. The main outcome was hazardous drinking defined by an AUDIT score >= 8 in men and >= 5 in women. Results: 69.0% of attendees were recruited, of whom 89.5% participated again after six months. The risk factors in the final predictAL model were sex, age, country, baseline AUDIT score, panic syndrome and lifetime alcohol problem. The predictAL model's average c-index across all six European countries was 0.839 (95% CI 0.805, 0.873). The Hedge's g effect size for the difference in log odds of predicted probability between safe drinkers in Europe who subsequently developed hazardous alcohol use and those who did not was 1.38 (95% CI 1.25, 1.51). External validation of the algorithm in Chilean safe drinkers resulted in a c-index of 0.781 (95% CI 0.717, 0.846) and Hedge's g of 0.68 (95% CI 0.57, 0.78). Conclusions: The predictAL risk model for development of hazardous consumption in safe drinkers compares favourably with risk algorithms for disorders in other medical settings and can be a useful first step in prevention of alcohol misuse

What factors affect patients' recall of general practitioners' advice?

<p>Abstract</p> <p>Background</p> <p>In order for patients to adhere to advice, provided by family doctors, they must be able to recall it afterwards. However, several studies have shown that most patients do not fully understand or memorize it. The aim of this study was to determine the influence of demographic characteristics, education, amount of given advice and the time between consultations on recalled advice.</p> <p>Methods</p> <p>A prospective survey, lasting 30 months, was conducted in an urban family practice in Slovenia. Logistic regression analysis was used to identify the risk factors for poorer recall.</p> <p>Results</p> <p>250 patients (87.7% response rate) received at least one and up to four pieces of advice (2.4 ± 0.8). A follow-up consultation took place at 47.4 ± 35.2 days. The determinants of better recall were high school (OR 0.4, 95% CI 0.15-0.99, p = 0.049) and college education (OR 0.3, 95% CI 0.10-1.00, p = 0.050), while worse recall was determined by number of given instructions three or four (OR 26.1, 95% CI 3.15-215.24, p = 0.002; OR 56.8, 95% CI 5.91-546.12, p < 0.001, respectively) and re-test interval: 15-30 days (OR 3.3, 95% CI 1.06-10.13, p = 0.040), 31-60 days (OR 3.2, 95% CI 1.28-8.07, p = 0.013) and more than 60 days (OR 2.5, 95% CI 1.05-6.02, p = 0.038).</p> <p>Conclusions</p> <p>Education was an important determinant factor and warrants further study. Patients should be given no more than one or two instructions in a consultation. When more is needed, the follow-up should be within the next 14 days, and would be of a greater benefit to higher educated patients.</p

Report of the 12th Liaison Meeting

The 12th Liaison meeting was held in Brussels on 8th and 9th October 2015 to address the following Terms of Reference: TOR 1. Discussion on possible follow-­‐‑up to the main outputs/recommendations of: • The 2015 RCMs -­‐‑ specific recommendations addressed to the Liaison Meeting • PGECON, PGDATA, PGMed – outcomes and recommendations from their 2015 meeting • STECF EWG and STECF Plenary -­‐‑ outcomes and recommendations from their 2015 meetings • Data end users (ICES, STECF, RFMOs – GFCM, IATTC, ICCAT, IOTC, WCPFC, NAFO, SPRFMO, CECAF, WECAFC) TOR2. End user feedback on data transmission and related issues • Discuss feedback received from data end-­‐‑users on data transmission: main issues and possible harmonization of end user feedback to the Commission • JRC data transmission IT platform: experience gained and future steps • Discuss best practices on automatization of data upload by MS: data validation tools used by end users • Discussion on new set-­‐‑up for STECF evaluation of AR2014 & data transmission 2014 used in 2015 – continue like this next year? • Harmonisation and dissemination of DCF metadata: codelists, metiers, nomenclatures, best practices, standards • RCM data calls – overview of how MS responded TOR 3. Regional cooperation • Call for proposals MARE/2014/19 'ʹStrengthening Regional Cooperation in the area of fisheries data collection– state of play'ʹ. Presentation by a representative of the two RCG grants and discussions by LM thereafter. What should be the way forward? • Regional databases • Overview of use of the Regional Databases for RCMs in 2015 and problems identified • Other developments (RDB trainings in 2015, RDB Med&BS development) • Changes for the future – any recommendations from the LM? • Future role of RCMs and DCF-­‐‑related meetings: best practices, coordination, cohesion and common structure in line with emerging needs of DCF TOR 4. EU MAP • Discuss recommendations/ output of RCMs: List of proposed stocks, landing obligation, metiers • Discuss design-­‐‑based sampling in relation to DCF: does it fulfil DCF requirements? TOR 5. Availability of data • Overview of latest developments (DCF Database Feasibility Study and plans for a follow-­‐‑up study to this) TOR 6. AOB • Agree on a list of recommendations relating to DCF (that MS will need to report on in their AR2015) – COM will provide a compilation of proposed recommendations from LM & STECF Plenaries in 2014 as input • Prepare a list of recommended meetings for 2016 as guidance for MS • Review and prioritize DCF-­‐‑related study proposals from RCMs, PGECON, EGs etc • ICES update on workshop on concurrent sampling and plans to re-­‐‑evaluate survey

The breadth of primary care: a systematic literature review of its core dimensions

Background: Even though there is general agreement that primary care is the linchpin of effective health care delivery, to date no efforts have been made to systematically review the scientific evidence supporting this supposition. The aim of this study was to examine the breadth of primary care by identifying its core dimensions and to assess the evidence for their interrelations and their relevance to outcomes at (primary) health system level. Methods: A systematic review of the primary care literature was carried out, restricted to English language journals reporting original research or systematic reviews. Studies published between 2003 and July 2008 were searched in MEDLINE, Embase, Cochrane Library, CINAHL, King's Fund Database, IDEAS Database, and EconLit. Results: Eighty-five studies were identified. This review was able to provide insight in the complexity of primary care as a multidimensional system, by identifying ten core dimensions that constitute a primary care system. The structure of a primary care system consists of three dimensions: 1. governance; 2. economic conditions; and 3. workforce development. The primary care process is determined by four dimensions: 4. access; 5. continuity of care; 6. coordination of care; and 7. comprehensiveness of care. The outcome of a primary care system includes three dimensions: 8. quality of care; 9. efficiency care; and 10. equity in health. There is a considerable evidence base showing that primary care contributes through its dimensions to overall health system performance and health. Conclusions: A primary care system can be defined and approached as a multidimensional system contributing to overall health system performance and health

Family medicine in post-communist Europe needs a boost. Exploring the position of family medicine in healthcare systems of Central and Eastern Europe and Russia

<p>Abstract</p> <p>Background</p> <p>The countries of Central and Eastern Europe have experienced a lot of changes at the end of the 20th century, including changes in the health care systems and especially in primary care. The aim of this paper is to systematically assess the position of family medicine in these countries, using the same methodology within all the countries.</p> <p>Methods</p> <p>A key informants survey in 11 Central and Eastern European countries and Russia using a questionnaire developed on the basis of systematic literature review.</p> <p>Results</p> <p>Formally, family medicine is accepted as a specialty in all the countries, although the levels of its implementation vary across the countries and the differences are important. In most countries, solo practice is the most predominant organisational form of family medicine. Family medicine is just one of many medical specialties (e.g. paediatrics and gynaecology) in primary health care. Full introduction of family medicine was successful only in Estonia.</p> <p>Conclusions</p> <p>Some of the unification of the systems may have been the result of the EU request for adequate training that has pushed the policies towards higher standards of training for family medicine. The initial enthusiasm of implementing family medicine has decreased because there was no initiative that would support this movement. Internal and external stimuli might be needed to continue transition process.</p

Elimination of a group II intron from a plastid gene causes a mutant phenotype

Group II introns are found in bacteria and cell organelles (plastids, mitochondria) and are thought to represent the evolutionary ancestors of spliceosomal introns. It is generally believed that group II introns are selfish genetic elements that do not have any function. Here, we have scrutinized this assumption by analyzing two group II introns that interrupt a plastid gene (ycf3) involved in photosystem assembly. Using stable transformation of the plastid genome, we have generated mutant plants that lack either intron 1 or intron 2 or both. Interestingly, the deletion of intron 1 caused a strong mutant phenotype. We show that the mutants are deficient in photosystem I and that this deficiency is directly related to impaired ycf3 function. We further show that, upon deletion of intron 1, the splicing of intron 2 is strongly inhibited. Our data demonstrate that (i) the loss of a group II intron is not necessarily phenotypically neutral and (ii) the splicing of one intron can depend on the presence of another

Insensitivity of chloroplast gene expression to DNA methylation

Presence and possible functions of DNA methylation in plastid genomes of higher plants have been highly controversial. While a number of studies presented evidence for the occurrence of both cytosine and adenine methylation in plastid genomes and proposed a role of cytosine methylation in the transcriptional regulation of plastid genes, several recent studies suggested that at least cytosine methylation may be absent from higher plant plastid genomes. To test if either adenine or cytosine methylation can play a regulatory role in plastid gene expression, we have introduced cyanobacterial genes for adenine and cytosine DNA methyltransferases (methylases) into the tobacco plastid genome by chloroplast transformation. Using DNA cleavage with methylation-sensitive and methylation-dependent restriction endonucleases, we show that the plastid genomes in the transplastomic plants are efficiently methylated. All transplastomic lines are phenotypically indistinguishable from wild-type plants and, moreover, show no alterations in plastid gene expression. Our data indicate that the expression of plastid genes is not sensitive to DNA methylation and, hence, suggest that DNA methylation is unlikely to be involved in the transcriptional regulation of plastid gene expression

Offline eLearning for undergraduates in health professions: A systematic review of the impact on knowledge, skills, attitudes and satisfaction

Background: The world is short of 7.2 million health–care workers and this figure is growing. The shortage of teachers is even greater, which limits traditional education modes. eLearning may help overcome this training need. Offline eLearning is useful in remote and resource–limited settings with poor internet access. To inform investments in offline eLearning, we need to establish its effectiveness in terms of gaining knowledge and skills, students’ satisfaction and attitudes towards eLearning. Methods: We conducted a systematic review of offline eLearning for students enrolled in undergraduate, health–related university degrees. We included randomised controlled trials that compared offline eLearning to traditional learning or an alternative eLearning method. We searched the major bibliographic databases in August 2013 to identify articles that focused primarily on students’ knowledge, skills, satisfaction and attitudes toward eLearning, and health economic information and adverse effects as secondary outcomes. We also searched reference lists of relevant studies. Two reviewers independently extracted data from the included studies. We synthesized the findings using a thematic summary approach. Findings: Forty–nine studies, including 4955 students enrolled in undergraduate medical, dentistry, nursing, psychology, or physical therapy studies, met the inclusion criteria. Eleven of the 33 studies testing knowledge gains found significantly higher gains in the eLearning intervention groups compared to traditional learning, whereas 21 did not detect significant differences or found mixed results. One study did not test for differences. Eight studies detected significantly higher skill gains in the eLearning intervention groups, whilst the other 5 testing skill gains did not detect differences between groups. No study found offline eLearning as inferior. Generally no differences in attitudes or preference of eLearning over traditional learning were observed. No clear trends were found in the comparison of different modes of eLearning. Most of the studies were small and subject to several biases. Conclusions: Our results suggest that offline eLearning is equivalent and possibly superior to traditional learning regarding knowledge, skills, attitudes and satisfaction. Although a robust conclusion cannot be drawn due to variable quality of the evidence, these results justify further investment into offline eLearning to address the global health care workforce shortage