Explore the vast range of titles available.

A search strategy for a systematic review that uses the Population, Intervention, Comparison, and Outcome framework should include the population, the intervention(s), and the type(s) of study design. According to existing guidelines, outcome should generally be excluded from the search strategy unless the search is multistranded. However, a recent study found that approximately 10% (51) of recent Cochrane reviews on interventions included outcomes in their literature search strategies. This study aims to analyze the alternatives to including outcomes in a search strategy by analyzing these recent Cochrane reviews. This study analyzes the 51 Cochrane reviews that included outcomes in their literature search strategies and analyzes the results of alternative search strategies that follow current recommendations. Despite a small study sample of 51 reviews the results show that many of the reviews excluded some of the recommended elements due to very broadly defined elements (e.g., all interventions or all people). Furthermore, excluding outcomes from the search strategy is followed by an enormous increase in the number of retrieved records making it unmanageable to screen, if using a single-stranded search strategy. Recommendations for search strategies in difficult cases are called for.

Objective: This review aimed to determine if the use of the patient, intervention, comparison, outcome (PICO) model as a search strategy tool affects the quality of a literature search.Methods: A comprehensive literature search was conducted in PubMed, Embase, CINAHL, PsycINFO, Cochrane Library, Web of Science, Library and Information Science Abstracts (LISA), Scopus, and the National Library of Medicine (NLM) catalog up until January 9, 2017. Reference lists were scrutinized, and citation searches were performed on the included studies. The primary outcome was the quality of literature searches and the secondary outcome was time spent on the literature search when the PICO model was used as a search strategy tool, compared to the use of another conceptualizing tool or unguided searching.Results: A total of 2,163 records were identified, and after removal of duplicates and initial screening, 22 full-text articles were assessed. Of these, 19 studies were excluded and 3 studies were included, data were extracted, risk of bias was assessed, and a qualitative analysis was conducted. The included studies compared PICO to the PIC truncation or links to related articles in PubMed, PICOS, and sample, phenomenon of interest, design, evaluation, research type (SPIDER). One study compared PICO to unguided searching. Due to differences in intervention, no quantitative analysis was performed.Conclusions: Only few studies exist that assess the effect of the PICO model vis-a-vis other available models or even vis-a-vis the use of no model. Before implications for current practice can be drawn, well-designed studies are needed to evaluate the role of the tool used to devise a search strategy. This article has been approved for the Medical Library Association’s Independent Reading Program.

In environments with scarce resources, adopting the right search strategy can make the difference between succeeding and failing, even between life and death. At different scales, this applies to molecular encounters in the cell cytoplasm, to animals looking for food or mates in natural landscapes, to rescuers during search and rescue operations in disaster zones, and to genetic computer algorithms exploring parameter spaces. When looking for sparse targets in a homogeneous environment, a combination of ballistic and diffusive steps is considered optimal; in particular, more ballistic Lévy flights with exponent α ≤ 1 are generally believed to optimize the search process. However, most search spaces present complex topographies. What is the best search strategy in these more realistic scenarios? Here, we show that the topography of the environment significantly alters the optimal search strategy toward less ballistic and more Brownian strategies. We consider an active particle performing a blind cruise search for nonregenerating sparse targets in a 2D space with steps drawn from a Lévy distribution with the exponent varying from α = 1 to α = 2 (Brownian). We show that, when boundaries, barriers, and obstacles are present, the optimal search strategy depends on the topography of the environment, with α assuming intermediate values in the whole range under consideration. We interpret these findings using simple scaling arguments and discuss their robustness to varying searcher’s size. Our results are relevant for search problems at different length scales from animal and human foraging to microswimmers’ taxis to biochemical rates of reaction.

PurposeThe present study aimed to understand how participation in university extracurricular activities has a beneficial or detrimental impact on students’ employment self-efficacy through the intervening mechanism of information search strategy.Design/methodology/approachThe authors collected data from active job-searching university students across two time-points and hypothesized that the breadth of extracurricular activity participation would positively impact employment self-efficacy perceptions and information search strategies (focused, exploratory and haphazard) would mediate this relationship.FindingsResults indicate that the breadth of students' participation in extracurricular activities was positively associated with employment self-efficacy perceptions, and this relationship was mediated by focused and exploratory information-search strategies. Extracurricular activities exhibited a negative relationship with a haphazard search strategy.Research limitations/implicationsThis research extends the understanding of the role of participation in extracurricular activities for influencing a job search. Future research may replicate these findings with different samples of job seekers.Practical implicationsExtracurricular activities are typically offered at universities as a way for students to develop skills and to improve employers' perceptions of students. The present results suggest that participating in extracurricular activities may also help university students to effectively conduct a self-directed job search.Originality/valueWe examined the role of extracurricular activities from the applicant's perspective, extending prior research examining extracurricular activities from the employer's perspective. The present results suggest that extracurricular activities play an important role in shaping the job search process of university students by influencing students' confidence for finding employment. Information search strategies mediated the effects of extracurricular activities on employment self-efficacy perceptions, suggesting that participating in extracurricular activities changed the way that applicants searched for jobs.

External knowledge search strategies are considered essential for increasing export performance, a crucial goal for small and medium enterprises (SMEs) in a globalised and turbulent environment. SMEs are known to have limited resources, which leads them to choose the export strategy as the best alternative for entering foreign markets. The present study analyses the link between industrial and nonindustrial knowledge search strategies and export performance, taking into account absorptive capacity (AC) as a mediating variable. Results from a sample of 222 Spanish exporting SMEs reveal that orientation to collaborate with industrial partners contributes to firms' AC and export performance. Moreover, AC is found to have a full mediating role between orientation to collaborate with industrial partners and export performance. The study makes a novel contribution by applying organisational learning theory to explain how both the strategies firms adopt to access external knowledge and their absorptive capacity affect their export performance.

Purpose The purpose of this study is to explore organizational factors that act as antecedents of open innovation search. The authors aim to empirically examine whether the extent to which the organizational slack is absorbed determines its influence on firms’ openness in innovation search. In addition, the authors also examine the moderating effect of absorptive capacity on the relationship between slack and open innovation search. Design/methodology/approach This study adopted secondary data from multiple sources (NBER, Compustat and US census) and then constructed a ten-year balanced panel dataset of 298 manufacturers. The generalized least square method was used to explore the determinants of open innovation search among manufacturing firms. Findings The results of this study reveal that the absorption level of organizational slack indeed determines the openness in innovation search. Specifically, absorbed slack negatively affects a firm’s openness in innovation search, whereas unabsorbed slack promotes open innovation search. Additionally, the relationship between absorbed slack and open innovation search will be less negative with the increase of absorptive capacity. Originality/value Different from most previous studies that have examined the performance effect of open search among high-tech and large enterprises, this study focuses on the antecedents of open search strategy in both high- and low-tech, large and small firms. The findings reveal that different forms of organizational slack divergently influence a firm’s open search strategy, contributing to the understanding of the relationship between organizational slack and knowledge search behavior in a broader context, as well as the understanding of the moderating effect of absorptive capacity.

With the increase in the use of the internet to search for health information about health-related problems, there is a need for health care professionals to better understand how their patients search for and use the online health information that may influence their medical decision making. The aims of this study are to explore laypeople’s online health information search strategies and examine the relationships between their search strategies and utilization behavior of online health information. Two scales, namely match and elaboration, were used to measure patients’ basic search strategies (ie, simple approach) and advanced search strategies (ie, integrative approach), respectively. In addition, the consultation scale was used to evaluate the participants’ use of online health information to consult doctors and others. A total of 253 outpatients without university education were purposely selected and surveyed. The participants were outpatients at a university-affiliated teaching hospital. Partial least squares-structural equation modeling (PLS-SEM) was performed to analyze the measurement model to specify the measurement validation. In addition, the structure model of PLS-SEM was evaluated to examine the path correlations between variables and to execute interaction effect and curvilinear relationship analyses. The results of the path correlation analysis by PLS-SEM showed that both elaboration strategy (path coefficient=0.55, P<.001) and match strategy (path coefficient=0.36, P<.001) were positively correlated with consultation on online health information with doctors and others. In addition, interaction effect and curvilinear relationship analyses indicated that there was a significant interaction effect between elaboration and match on consultation (path coefficient=–0.34, P<.001) and a significant curvilinear relationship between match and consultation (path coefficient=–0.09, P=.046). Increasing patients’ exposure to online health information through both a simple search approach (ie, match strategy) and a complex search approach (ie, elaboration strategy) may lead them to appropriately use the information to consult doctors and others. However, the results of interaction effect and curvilinear relationship analyses highlighted the essential role of the elaboration strategy to properly locate, evaluate, and apply online health information. The findings of this study may help health care professionals better understand how to communicate with their patients through the health information on the internet.

In this paper, a novel optimization method is proposed based on the sparrow search algorithm, namely, multi-strategy-guided sparrow search algorithm (MGSSA). It is well-known that the basic SSA has limitations such as the slow convergence speed and vulnerability to local optimality. Surrounding these two issues, some strategies are presented in the MGSSA. Firstly, the newly introduced ring topology search strategy not only maintains the diversity of the entire population but also enhances the exploration ability of the SSA. Secondly, the proposed leader-based search strategy can improve exploitation ability of the SSA to prevent falling into local optimum as much as possible. Moreover, the coordinated learning strategy is put forward to better balance between the exploration and exploitation abilities. Finally, the MGSSA is compared with seventeen advanced algorithms on two well-known benchmark suites (i.e., CEC-2017 and CEC-2020). Meanwhile, the MGSSA-based forecasting approach is applied to predict the remaining useful life for lithium-ion batteries. The statistical results indicate that the MGSSA is a high-performance optimizer, which can not only solve the defects of the original SSA, but also obtain satisfactory solutions in both complex numerical optimization and real-world application problems.

Distributed Constraint Optimization Problem (DCOP) is a promising framework for modeling a wide variety of multi-agent coordination problems. Best-First search (BFS) and Depth-First search (DFS) are two main search strategies used for search-based complete DCOP algorithms. Unfortunately, BFS often has to deal with a large number of solution reconstructions whereas DFS is unable to promptly prune sub-optimal branch. However, their weaknesses will be remedied if the two search strategies are combined based on agents’ positions in a pseudo-tree. Therefore, a hybrid DCOP algorithm with the combination of BFS and DFS, called BD-ADOPT, is proposed, in which a layering boundary is introduced to divide all agents into BFS-based agents and DFS-based agents. Furthermore, this paper gives a rule to find a suitable layering boundary with a new strategy for the agents near the boundary to realize the seamless joint between BFS and DFS strategies. Detailed experimental results show that BD-ADOPT outperforms some famous search-based complete DCOP algorithms on the benchmark problems.