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Research synthesis on simple yet general hypotheses and ideas is challenging in scientific disciplines studying highly context-dependent systems such as medical, social, and biological sciences. This study shows that machine learning, equation-free statistical modeling of artificial intelligence, is a promising synthesis tool for discovering novel patterns and the source of controversy in a general hypothesis. We apply a decision tree algorithm, assuming that evidence from various contexts can be adequately integrated in a hierarchically nested structure. As a case study, we analyzed 163 articles that studied a prominent hypothesis in invasion biology, the enemy release hypothesis. We explored if any of the nine attributes that classify each study can differentiate conclusions as classification problem. Results corroborated that machine learning can be useful for research synthesis, as the algorithm could detect patterns that had been already focused in previous narrative reviews. Compared with the previous synthesis study that assessed the same evidence collection based on experts' judgement, the algorithm has newly proposed that the studies focusing on Asian regions mostly supported the hypothesis, suggesting that more detailed investigations in these regions can enhance our understanding of the hypothesis. We suggest that machine learning algorithms can be a promising synthesis tool especially where studies (a) reformulate a general hypothesis from different perspectives, (b) use different methods or variables, or (c) report insufficient information for conducting meta-analyses.
This study examined the effectiveness of psychological interventions for severe health anxiety (SHA) regarding somatic symptoms (SS) and health anxiety (HA). The databases Web of Science, EBSCO, and CENTRAL were searched on May 15, 2019, May 16, 2019, and August 5, 2019, respectively. Eighteen randomized controlled trials (N = 2,050) met the inclusion criteria (i.e., hypochondriasis, illness anxiety disorder or somatic symptom disorder with elevated HA being assessed with validated interviews: use of standardized outcome measures). Two reviewers independently evaluated the studies' risk of bias using the Revised Cochrane Risk-of-Bias Tool for randomized trials (RoB-2) tool. Overall, psychological interventions were significantly more effective than waitlist, treatment-as-usual, or placebo post-treatment (g(SS) = 0.70, g(HA) = 1.11) and at follow-up (g(SS) = 0.33, g(HA)= 0.70). CBT outperformed other psychological interventions or pharmacotherapy for HA post- treatment (Hedge's g(HA) = 0.81). The number of sessions did not significantly predict the effect sizes. In sum, psychological interventions were effective for SHA, but the generalizability of the results for SS is limited, because only two high-quatity trials contributed to the comparison.
Research synthesis on simple yet general hypotheses and ideas is challenging in scientific disciplines studying highly context-dependent systems such as medical, social, and biological sciences. This study shows that machine learning, equation-free statistical modeling of artificial intelligence, is a promising synthesis tool for discovering novel patterns and the source of controversy in a general hypothesis. We apply a decision tree algorithm, assuming that evidence from various contexts can be adequately integrated in a hierarchically nested structure. As a case study, we analyzed 163 articles that studied a prominent hypothesis in invasion biology, the enemy release hypothesis. We explored if any of the nine attributes that classify each study can differentiate conclusions as classification problem. Results corroborated that machine learning can be useful for research synthesis, as the algorithm could detect patterns that had been already focused in previous narrative reviews. Compared with the previous synthesis study that assessed the same evidence collection based on experts' judgement, the algorithm has newly proposed that the studies focusing on Asian regions mostly supported the hypothesis, suggesting that more detailed investigations in these regions can enhance our understanding of the hypothesis. We suggest that machine learning algorithms can be a promising synthesis tool especially where studies (a) reformulate a general hypothesis from different perspectives, (b) use different methods or variables, or (c) report insufficient information for conducting meta-analyses.
This systematic review investigated how successful children/adolescents with poor literacy skills learn a foreign language compared with their peers with typical literacy skills. Moreover, we explored whether specific characteristics related to participants, foreign language instruction, and assessment moderated scores on foreign language tests in this population. Overall, 16 studies with a total of 968 participants (poor reader/spellers:n = 404; control participants:n = 564) met eligibility criteria. Only studies focusing on English as a foreign language were available. Available data allowed for meta-analyses on 10 different measures of foreign language attainment. In addition to standard mean differences (SMDs), we computed natural logarithms of the ratio of coefficients of variation (CVRs) to capture individual variability between participant groups. Significant between-study heterogeneity, which could not be explained by moderator analyses, limited the interpretation of results. Although children/adolescents with poor literacy skills on average showed lower scores on foreign language phonological awareness, letter knowledge, and reading comprehension measures, their performance varied significantly more than that of control participants. Thus, it remains unclear to what extent group differences between the foreign language scores of children/adolescents with poor and typical literacy skills are representative of individual poor readers/spellers. Taken together, our results indicate that foreign language skills in children/adolescents with poor literacy skills are highly variable. We discuss the limitations of past research that can guide future steps toward a better understanding of individual differences in foreign language attainment of children/adolescents with poor literacy skills.
This systematic review investigated how successful children/adolescents with poor literacy skills learn a foreign language compared with their peers with typical literacy skills. Moreover, we explored whether specific characteristics related to participants, foreign language instruction, and assessment moderated scores on foreign language tests in this population. Overall, 16 studies with a total of 968 participants (poor reader/spellers:n = 404; control participants:n = 564) met eligibility criteria. Only studies focusing on English as a foreign language were available. Available data allowed for meta-analyses on 10 different measures of foreign language attainment. In addition to standard mean differences (SMDs), we computed natural logarithms of the ratio of coefficients of variation (CVRs) to capture individual variability between participant groups. Significant between-study heterogeneity, which could not be explained by moderator analyses, limited the interpretation of results. Although children/adolescents with poor literacy skills on average showed lower scores on foreign language phonological awareness, letter knowledge, and reading comprehension measures, their performance varied significantly more than that of control participants. Thus, it remains unclear to what extent group differences between the foreign language scores of children/adolescents with poor and typical literacy skills are representative of individual poor readers/spellers. Taken together, our results indicate that foreign language skills in children/adolescents with poor literacy skills are highly variable. We discuss the limitations of past research that can guide future steps toward a better understanding of individual differences in foreign language attainment of children/adolescents with poor literacy skills.