{"id":"W2793972423","doi":"10.1007/s10823-018-9344-x","title":"Validation of a Social Networks and Support Measurement Tool for Use in International Aging Research: The International Mobility in Aging Study","year":2018,"lang":"en","type":"article","venue":"Journal of Cross-Cultural Gerontology","topic":"Health disparities and outcomes","field":"Social Sciences","cited_by":25,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University; Université de Montréal","funders":"Institute of Aging; Universidade Federal do Rio Grande do Norte; Universidad de Caldas","keywords":"Psychology; Confirmatory factor analysis; Goodness of fit; Likert scale; Social support; Sample (material); Scale (ratio); Reliability (semiconductor); Social network (sociolinguistics); Developmental psychology; Structural equation modeling; Social psychology; Gerontology; Clinical psychology; Social media; Medicine; Statistics; Computer science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.02006959,0.000890623,0.001224452,0.002569323,0.002577645,0.002888959,0.002172174,0.001717982,0.002173221],"category_scores_gemma":[0.03542256,0.0008024815,0.002213641,0.001992064,0.0009405786,0.002411943,0.003785776,0.002097774,0.0007933926],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0013805,"about_ca_system_score_gemma":0.004640736,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01137948,"about_ca_topic_score_gemma":0.02175244,"domain_scores_codex":[0.9939604,0.002272938,0.001156964,0.0006513619,0.001612121,0.0003462037],"domain_scores_gemma":[0.9829319,0.005388662,0.002527603,0.00149218,0.006500689,0.00115897],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007027376,0.002933236,0.944346,0.0002908889,0.0005944965,0.000107652,0.004596082,0.0004695216,0.0008722953,0.000680551,0.006798638,0.03760796],"study_design_scores_gemma":[0.0004737747,0.001183257,0.9843162,0.0001726813,0.0003441079,0.0001605155,0.003641992,0.001707924,0.0006746587,0.0006079155,0.006628887,0.00008815824],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.981985,0.000265859,0.003915533,0.0006381868,0.0002627772,0.005360187,0.003717827,0.00007038896,0.003784144],"genre_scores_gemma":[0.9427462,0.0004934195,0.02015056,0.0006729574,0.0001389551,0.02427556,0.00838972,0.000106014,0.003026748],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9799304,"threshold_uncertainty_score":0.1061394,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2941334258412635,"score_gpt":0.5196508338623749,"score_spread":0.2255174080211114,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}