{"id":"W2323260053","doi":"10.1037/a0035297","title":"The separation of between-person and within-person components of individual change over time: A latent curve model with structured residuals.","year":2013,"lang":"en","type":"article","venue":"Journal of Consulting and Clinical Psychology","topic":"Mental Health Research Topics","field":"Psychology","cited_by":487,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"National Institute on Drug Abuse; National Institutes of Health","keywords":"Psychology; Latent variable model; Set (abstract data type); Stability (learning theory); Reciprocal; Latent variable; Latent growth modeling; Growth curve (statistics); Multivariate statistics; Regression; Statistical model; Cognitive psychology; Artificial intelligence; Machine learning; Econometrics; Developmental psychology; Computer science; Mathematics; Psychotherapist","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01277106,0.001137582,0.001133774,0.002322486,0.0006379535,0.002975753,0.002879476,0.002125946,0.004938264],"category_scores_gemma":[0.05907732,0.0007046668,0.002394763,0.003367995,0.003405906,0.004278332,0.002692157,0.002879783,0.001564424],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002504433,"about_ca_system_score_gemma":0.002655949,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.012032,"about_ca_topic_score_gemma":0.008092497,"domain_scores_codex":[0.9943647,0.003223113,0.0002504328,0.001134727,0.0006662908,0.0003606848],"domain_scores_gemma":[0.9746091,0.01729711,0.003139828,0.002649882,0.001612721,0.0006913507],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006996696,0.0006736297,0.1337686,0.0005854165,0.0007649322,0.0004682541,0.007805765,0.2208897,0.001962844,0.4772277,0.006176727,0.1489767],"study_design_scores_gemma":[0.00008553379,0.0003733587,0.0347999,0.0001943841,0.0002069037,0.0002755188,0.00101992,0.7118579,0.0005827112,0.2403069,0.01015229,0.0001445998],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1222305,0.000854246,0.8672891,0.002519351,0.0001244382,0.0004106258,0.00127568,0.0007564653,0.004539498],"genre_scores_gemma":[0.8584253,0.0009150772,0.1312133,0.0002097648,0.0000922963,0.001203077,0.001684449,0.0002920669,0.005964683],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01277106,"threshold_uncertainty_score":0.06754059,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3070934735097968,"score_gpt":0.5104513217750134,"score_spread":0.2033578482652166,"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."}}