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"Search Strategies"
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Avoiding searching for outcomes called for additional search strategies: a study of Cochrane review searches
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.
Journal Article
The impact of patient, intervention, comparison, outcome (PICO) as a search strategy tool on literature search quality: a systematic review
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.
Journal Article
The topography of the environment alters the optimal search strategy for active particles
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.
Journal Article
The role of extracurricular activities in shaping university students' employment self-efficacy perceptions
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.
Journal Article
Added Value of Medical Subject Headings Terms in Search Strategies of Systematic Reviews: Comparative Study
The massive increase in the number of published scientific articles enhances knowledge but makes it more complicated to summarize results. The Medical Subject Headings (MeSH) thesaurus was created in the mid-20th century with the aim of systematizing article indexing and facilitating their retrieval. Despite the advent of search engines, few studies have questioned the relevance of the MeSH thesaurus, and none have done so systematically.
The objective of this study was to estimate the added value of using MeSH terms in PubMed queries for systematic reviews (SRs).
SRs published in 4 high-impact medical journals in general medicine over the past 10 years were selected. Only SRs for which a PubMed query was provided were included. Each query was transformed to obtain 3 versions: the original query (V1), the query with free-text terms only (V2), and the query with MeSH terms only (V3). These 3 queries were compared with each other based on their sensitivity and positive predictive values.
In total, 59 SRs were included. The suppression of MeSH terms had an impact on the number of relevant articles retrieved for 24 (41%) out of 59 SRs. The median (IQR) sensitivities of queries V1 and V2 were 77.8% (62.1%-95.2%) and 71.4% (42.6%-90%), respectively. V1 queries provided an average of 2.62 additional relevant papers per SR compared with V2 queries. However, an additional 820.29 papers had to be screened. The cost of screening an additional collected paper was therefore 313.09, which was slightly more than triple the mean reading cost associated with V2 queries (88.67).
Our results revealed that removing MeSH terms from a query decreases sensitivity while slightly increasing the positive predictive value. Queries containing both MeSH and free-text terms yielded more relevant articles but required screening many additional papers. Despite this additional workload, MeSH terms remain indispensable for SRs.
Journal Article
Tracking developments in artificial intelligence research: constructing and applying a new search strategy
Artificial intelligence, as an emerging and multidisciplinary domain of research and innovation, has attracted growing attention in recent years. Delineating the domain composition of artificial intelligence is central to profiling and tracking its development and trajectories. This paper puts forward a bibliometric definition for artificial intelligence which can be readily applied, including by researchers, managers, and policy analysts. Our approach starts with benchmark records of artificial intelligence captured by using a core keyword and specialized journal search. We then extract candidate terms from high frequency keywords of benchmark records, refine keywords and complement with the subject category “artificial intelligence”. We assess our search approach by comparing it with other three recent search strategies of artificial intelligence, using a common source of articles from the Web of Science. Using this source, we then profile patterns of growth and international diffusion of scientific research in artificial intelligence in recent years, identify top research sponsors in funding artificial intelligence and demonstrate how diverse disciplines contribute to the multidisciplinary development of artificial intelligence. We conclude with implications for search strategy development and suggestions of lines for further research.
Journal Article
Export Performance in SMEs:The Importance of External Knowledge Search Strategies and Absorptive Capacity
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.
Journal Article
Open innovation search in manufacturing firms: the role of organizational slack and absorptive capacity
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.
Journal Article
A systematic method to create search strategies for emerging technologies based on the Web of Science: illustrated for ‘Big Data’
Bibliometric and “tech mining” studies depend on a crucial foundation—the search strategy used to retrieve relevant research publication records. Database searches for emerging technologies can be problematic in many respects, for example the rapid evolution of terminology, the use of common phraseology, or the extent of “legacy technology” terminology. Searching on such legacy terms may or may not pick up R&D pertaining to the emerging technology of interest. A challenge is to assess the relevance of legacy terminology in building an effective search model. Common-usage phraseology additionally confounds certain domains in which broader managerial, public interest, or other considerations are prominent. In contrast, searching for highly technical topics is relatively straightforward. In setting forth to analyze “Big Data,” we confront all three challenges—emerging terminology, common usage phrasing, and intersecting legacy technologies. In response, we have devised a systematic methodology to help identify research relating to Big Data. This methodology uses complementary search approaches, starting with a Boolean search model and subsequently employs contingency term sets to further refine the selection. The four search approaches considered are: (1) core lexical query, (2) expanded lexical query, (3) specialized journal search, and (4) cited reference analysis. Of special note here is the use of a “Hit-Ratio” that helps distinguish Big Data elements from less relevant legacy technology terms. We believe that such a systematic search development positions us to do meaningful analyses of Big Data research patterns, connections, and trajectories. Moreover, we suggest that such a systematic search approach can help formulate more replicable searches with high recall and satisfactory precision for other emerging technology studies.
Journal Article