{"id":"W4298006059","doi":"10.1007/s42844-022-00078-6","title":"Quantifying Resilience as an Outcome: Advancing the Residual Approach with Influence Statistics to Derive More Adequate Thresholds of Resilience","year":2022,"lang":"en","type":"article","venue":"Adversity and Resilience Science","topic":"Resilience and Mental Health","field":"Psychology","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dalhousie University","funders":"Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","keywords":"Statistics; Sample (material); Econometrics; Outcome (game theory); Context (archaeology); Psychology; Population; Resilience (materials science); Psychological resilience; Normative; Statistical model; Coping (psychology); Influencer marketing; Mathematics; Social psychology; Clinical psychology; Geography; Medicine; Economics; Environmental health","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":["sts"],"category_scores_codex":[0.002943896,0.0003591503,0.0004258392,0.0004171511,0.003914726,0.0001041791,0.00266678,0.00007253326,0.00008513882],"category_scores_gemma":[0.0003479408,0.0002699293,0.0000445958,0.003079129,0.004705421,0.001352151,0.001148864,0.000699326,0.00002148272],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002335622,"about_ca_system_score_gemma":0.000674807,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003037277,"about_ca_topic_score_gemma":0.000407957,"domain_scores_codex":[0.9938892,0.0003630647,0.0006354428,0.001611066,0.002131766,0.001369494],"domain_scores_gemma":[0.9969108,0.000385315,0.0004251694,0.001345211,0.000276597,0.0006568796],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002772436,0.0009626486,0.701165,0.0002139549,0.00003595276,0.0003588265,0.09200201,0.1130062,0.01326288,0.05010402,0.0006749842,0.02544109],"study_design_scores_gemma":[0.0008243921,0.003533328,0.7923108,0.00008914708,0.00005207821,0.0006605757,0.1972164,0.001849973,0.002009285,0.0003776932,0.0003485265,0.0007278491],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9917842,0.0002165536,0.004206284,0.000672615,0.0002893847,0.0009935229,0.00007482323,0.00005766669,0.001705023],"genre_scores_gemma":[0.9899734,0.00003742861,0.007446975,0.001779561,0.00002469554,0.00008121539,0.000004994616,0.00001804896,0.0006337115],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1111562,"threshold_uncertainty_score":0.9999753,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03026675616427283,"score_gpt":0.3806377899362784,"score_spread":0.3503710337720056,"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."}}