{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006473937,0.000251068,0.0005132391,0.0008086806,0.0007817091,0.0009450458,0.0006330509,0.0006492389,0.002591655],"category_scores_gemma":[0.02351666,0.0002754915,0.001047138,0.0007891693,0.0003700081,0.0006557407,0.000872508,0.001552917,0.000256409],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006550567,"about_ca_system_score_gemma":0.000937546,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005488074,"about_ca_topic_score_gemma":0.005214961,"domain_scores_codex":[0.9974418,0.0009683208,0.0002920635,0.0001595896,0.0007392904,0.0003989854],"domain_scores_gemma":[0.9884926,0.003756495,0.004617158,0.0004651367,0.0009184908,0.001750128],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002821127,0.0004111472,0.9949569,0.00001770382,0.00004707891,0.00001526904,0.0001794874,0.00003880757,0.00004719007,0.00001208596,0.00009668332,0.003895658],"study_design_scores_gemma":[0.00000966676,0.0004261786,0.9988587,0.0000209759,0.00002809098,0.00003206084,0.0001931321,0.0003001013,0.00004758124,0.00002171699,0.00005830492,0.000003442749],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9993013,0.0001013914,0.0001138692,0.00009151721,0.000005425264,0.00004108914,0.00009241636,0.000007662342,0.0002451062],"genre_scores_gemma":[0.9994218,0.00004247254,0.0001140797,0.00003269365,0.000005695812,0.00003502847,0.0001523779,0.000004236822,0.0001915714],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006473937,"threshold_uncertainty_score":0.03423792,"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."}}