PMC:7216275 / 33159-34609 JSONTXT

{"project":"2_test","denotations":[{"id":"32344770-22529829-49254362","span":{"begin":445,"end":447},"obj":"22529829"},{"id":"32344770-13880271-49254363","span":{"begin":559,"end":561},"obj":"13880271"},{"id":"32344770-13880271-49254364","span":{"begin":851,"end":853},"obj":"13880271"},{"id":"T27469","span":{"begin":445,"end":447},"obj":"22529829"},{"id":"T61498","span":{"begin":559,"end":561},"obj":"13880271"},{"id":"T60861","span":{"begin":851,"end":853},"obj":"13880271"}],"text":"The effect of the exogenous latent construct on the endogenous latent construct having three possible answers, i.e., substantial, moderate, and weak, was found using theF2 effect size. The blindfold method was used to check the strength of the research model. Cohen’s f2 is an identical measure of effect size that also permits checking the local effect size, which is the effect of one variable compared with the multivariate regression model [94]. If the cross-validated redundancy (Q2) is higher than 0,then the model is related to predicting that factor [95]. We focused on in-sample prediction more, compared to out-sample prediction, prognostic significance Q2, and relative relevance Q2, which are alternatives for evaluating a model’s practical relevance, in addition to consulting R2 outcomes as a gauge of a model’s predictive capabilities [95]. R2, Q2, path coefficients, and the effect size (f2) are the decisive factors we use for evaluation. In addition to this evaluation, researchers are required to check the inner model for potential co linearity issues. If the constructs are interrelated, then results approximated by the inner model are considered biased [96]. A model’s predictive accuracy is decided by the R2. The R2 value also characterizes the combined consequence of exogenous variables on the endogenous variable(s). The effect ranges from 0 to 1. A value of 1 indicates complete predictive accuracy as can see in Table 6."}

{"project":"LitCovid-PD-MONDO","denotations":[{"id":"T42","span":{"begin":790,"end":792},"obj":"Disease"},{"id":"T43","span":{"begin":856,"end":858},"obj":"Disease"},{"id":"T44","span":{"begin":1230,"end":1232},"obj":"Disease"},{"id":"T45","span":{"begin":1238,"end":1240},"obj":"Disease"}],"attributes":[{"id":"A42","pred":"mondo_id","subj":"T42","obj":"http://purl.obolibrary.org/obo/MONDO_0019903"},{"id":"A43","pred":"mondo_id","subj":"T43","obj":"http://purl.obolibrary.org/obo/MONDO_0019903"},{"id":"A44","pred":"mondo_id","subj":"T44","obj":"http://purl.obolibrary.org/obo/MONDO_0019903"},{"id":"A45","pred":"mondo_id","subj":"T45","obj":"http://purl.obolibrary.org/obo/MONDO_0019903"}],"text":"The effect of the exogenous latent construct on the endogenous latent construct having three possible answers, i.e., substantial, moderate, and weak, was found using theF2 effect size. The blindfold method was used to check the strength of the research model. Cohen’s f2 is an identical measure of effect size that also permits checking the local effect size, which is the effect of one variable compared with the multivariate regression model [94]. If the cross-validated redundancy (Q2) is higher than 0,then the model is related to predicting that factor [95]. We focused on in-sample prediction more, compared to out-sample prediction, prognostic significance Q2, and relative relevance Q2, which are alternatives for evaluating a model’s practical relevance, in addition to consulting R2 outcomes as a gauge of a model’s predictive capabilities [95]. R2, Q2, path coefficients, and the effect size (f2) are the decisive factors we use for evaluation. In addition to this evaluation, researchers are required to check the inner model for potential co linearity issues. If the constructs are interrelated, then results approximated by the inner model are considered biased [96]. A model’s predictive accuracy is decided by the R2. The R2 value also characterizes the combined consequence of exogenous variables on the endogenous variable(s). The effect ranges from 0 to 1. A value of 1 indicates complete predictive accuracy as can see in Table 6."}

{"project":"LitCovid-PD-CLO","denotations":[{"id":"T118","span":{"begin":268,"end":270},"obj":"http://purl.obolibrary.org/obo/CLO_0002972"},{"id":"T119","span":{"begin":445,"end":447},"obj":"http://purl.obolibrary.org/obo/CLO_0001527"},{"id":"T120","span":{"begin":567,"end":574},"obj":"http://purl.obolibrary.org/obo/CLO_0009985"},{"id":"T121","span":{"begin":733,"end":734},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T122","span":{"begin":805,"end":806},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T123","span":{"begin":816,"end":817},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T124","span":{"begin":904,"end":906},"obj":"http://purl.obolibrary.org/obo/CLO_0002972"},{"id":"T125","span":{"begin":1182,"end":1183},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T126","span":{"begin":1376,"end":1377},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"}],"text":"The effect of the exogenous latent construct on the endogenous latent construct having three possible answers, i.e., substantial, moderate, and weak, was found using theF2 effect size. The blindfold method was used to check the strength of the research model. Cohen’s f2 is an identical measure of effect size that also permits checking the local effect size, which is the effect of one variable compared with the multivariate regression model [94]. If the cross-validated redundancy (Q2) is higher than 0,then the model is related to predicting that factor [95]. We focused on in-sample prediction more, compared to out-sample prediction, prognostic significance Q2, and relative relevance Q2, which are alternatives for evaluating a model’s practical relevance, in addition to consulting R2 outcomes as a gauge of a model’s predictive capabilities [95]. R2, Q2, path coefficients, and the effect size (f2) are the decisive factors we use for evaluation. In addition to this evaluation, researchers are required to check the inner model for potential co linearity issues. If the constructs are interrelated, then results approximated by the inner model are considered biased [96]. A model’s predictive accuracy is decided by the R2. The R2 value also characterizes the combined consequence of exogenous variables on the endogenous variable(s). The effect ranges from 0 to 1. A value of 1 indicates complete predictive accuracy as can see in Table 6."}

{"project":"LitCovid-sentences","denotations":[{"id":"T282","span":{"begin":0,"end":184},"obj":"Sentence"},{"id":"T283","span":{"begin":185,"end":259},"obj":"Sentence"},{"id":"T284","span":{"begin":260,"end":449},"obj":"Sentence"},{"id":"T285","span":{"begin":450,"end":563},"obj":"Sentence"},{"id":"T286","span":{"begin":564,"end":855},"obj":"Sentence"},{"id":"T287","span":{"begin":856,"end":955},"obj":"Sentence"},{"id":"T288","span":{"begin":956,"end":1072},"obj":"Sentence"},{"id":"T289","span":{"begin":1073,"end":1181},"obj":"Sentence"},{"id":"T290","span":{"begin":1182,"end":1233},"obj":"Sentence"},{"id":"T291","span":{"begin":1234,"end":1344},"obj":"Sentence"},{"id":"T292","span":{"begin":1345,"end":1375},"obj":"Sentence"},{"id":"T293","span":{"begin":1376,"end":1450},"obj":"Sentence"}],"namespaces":[{"prefix":"_base","uri":"http://pubannotation.org/ontology/tao.owl#"}],"text":"The effect of the exogenous latent construct on the endogenous latent construct having three possible answers, i.e., substantial, moderate, and weak, was found using theF2 effect size. The blindfold method was used to check the strength of the research model. Cohen’s f2 is an identical measure of effect size that also permits checking the local effect size, which is the effect of one variable compared with the multivariate regression model [94]. If the cross-validated redundancy (Q2) is higher than 0,then the model is related to predicting that factor [95]. We focused on in-sample prediction more, compared to out-sample prediction, prognostic significance Q2, and relative relevance Q2, which are alternatives for evaluating a model’s practical relevance, in addition to consulting R2 outcomes as a gauge of a model’s predictive capabilities [95]. R2, Q2, path coefficients, and the effect size (f2) are the decisive factors we use for evaluation. In addition to this evaluation, researchers are required to check the inner model for potential co linearity issues. If the constructs are interrelated, then results approximated by the inner model are considered biased [96]. A model’s predictive accuracy is decided by the R2. The R2 value also characterizes the combined consequence of exogenous variables on the endogenous variable(s). The effect ranges from 0 to 1. A value of 1 indicates complete predictive accuracy as can see in Table 6."}