{"id":"W3514895","doi":"10.1007/978-3-642-31037-9_18","title":"Towards a Data-Driven Approach to Intervention Design: A Predictive Path Model of Healthy Eating Determinants","year":2012,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Behavioral Health and Interventions","field":"Psychology","cited_by":21,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Psychological intervention; Eating behavior; Intervention (counseling); Psychology; Healthy eating; Variance (accounting); Obesity; Path analysis (statistics); Variables; Applied psychology; Social psychology; Developmental psychology; Computer science; Medicine; Physical activity; Machine learning; Psychiatry","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":[0.01632345,0.001542629,0.002052647,0.001674996,0.0006836502,0.003242915,0.003154243,0.001827916,0.00470468],"category_scores_gemma":[0.05576898,0.0020927,0.001873302,0.001864832,0.001666921,0.003753185,0.002489808,0.004320737,0.0005899978],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001877488,"about_ca_system_score_gemma":0.004790423,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005798405,"about_ca_topic_score_gemma":0.00699053,"domain_scores_codex":[0.9924238,0.006096625,0.0002133513,0.000659986,0.0004793858,0.0001269658],"domain_scores_gemma":[0.9553389,0.04095789,0.0008018857,0.001122223,0.001438837,0.0003402831],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007540557,0.0007873583,0.009617085,0.001039324,0.0009499773,0.000202268,0.001403373,0.59276,0.001324768,0.1685165,0.00581845,0.2168269],"study_design_scores_gemma":[0.00009616922,0.0001314212,0.0006147996,0.0001039835,0.0001365554,0.00002794177,0.0001062471,0.8154892,0.0003042053,0.1818171,0.001142713,0.00002964732],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00452447,0.0000980698,0.9934505,0.0007157199,0.00002176887,0.0002053044,0.0002223754,0.0002128668,0.0005487872],"genre_scores_gemma":[0.1504256,0.0003195829,0.8452542,0.0003762631,0.00003863325,0.002111336,0.0005586578,0.0001250631,0.0007906224],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01632345,"threshold_uncertainty_score":0.08632767,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1797098440725429,"score_gpt":0.4027798277733501,"score_spread":0.2230699837008072,"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."}}