{"id":"W2525050280","doi":"10.2196/mhealth.5992","title":"Baseline Motivation Type as a Predictor of Dropout in a Healthy Eating Text Messaging Program","year":2016,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Behavioral Health and Interventions","field":"Psychology","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Cancer Institute","keywords":"Dropout (neural networks); Odds; Odds ratio; Logistic regression; Descriptive statistics; Psychology; Baseline (sea); Applied psychology; Gerontology; Computer science; Medicine; Statistics; Mathematics; Internal medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001122644,0.0001866323,0.0003903934,0.0003293619,0.0001501186,0.000009477239,0.0001146411,0.0001773081,0.0005448216],"category_scores_gemma":[0.0001544541,0.0001420041,0.00005431143,0.0005223129,0.0000996997,0.0001409506,0.00003362336,0.0002929671,0.00005544167],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001741235,"about_ca_system_score_gemma":0.000474682,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002272518,"about_ca_topic_score_gemma":0.0003327277,"domain_scores_codex":[0.9970727,0.000412389,0.001053208,0.0004559097,0.0002139873,0.0007918145],"domain_scores_gemma":[0.9983603,0.0002293215,0.0004382698,0.00030683,0.0001285226,0.0005368009],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0011482,0.001100817,0.525198,0.0008629992,0.000006996806,0.000005131982,0.001664327,1.317683e-7,0.00003062414,0.004999299,0.001709692,0.4632738],"study_design_scores_gemma":[0.003088408,0.005191274,0.9844541,0.0008577871,0.00001792659,0.00001813548,0.0007117125,0.00008604844,0.000006530625,0.000349091,0.005052267,0.0001667138],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9905117,0.001181852,0.00008809517,0.004410754,0.0004364615,0.001329839,0.00004027709,0.0001186544,0.001882436],"genre_scores_gemma":[0.9970341,0.0002948525,0.0005205399,0.001014618,0.0001383977,0.0003134722,0.00002654917,0.0000305154,0.000626991],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4631071,"threshold_uncertainty_score":0.5965414,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09769183745639451,"score_gpt":0.4675532419164647,"score_spread":0.3698614044600702,"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."}}