{"id":"W2297761788","doi":"10.1016/j.aap.2015.08.015","title":"What drives technology-based distractions? A structural equation model on social-psychological factors of technology-based driver distraction engagement","year":2016,"lang":"en","type":"article","venue":"Accident Analysis & Prevention","topic":"Human-Automation Interaction and Safety","field":"Psychology","cited_by":54,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"AUTO21 Network of Centres of Excellence; Toyota Collaborative Safety Research Center; University of Toronto","keywords":"Distraction; Structural equation modeling; Psychology; Social psychology; Applied psychology; Phone; Sensation seeking; Poison control; Distracted driving; Personality; Set (abstract data type); Human factors and ergonomics; Mobile phone; Engineering; Computer science; Cognitive psychology","routes":{"ca_aff":true,"ca_fund":true,"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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004284636,0.0003216132,0.0004629617,0.002178848,0.0003795385,0.00008428347,0.00031619,0.000472878,0.004959853],"category_scores_gemma":[0.0001446652,0.0002471698,0.0006384438,0.001473203,0.0001919119,0.0007362989,0.00003531123,0.0003591479,0.000158585],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003282514,"about_ca_system_score_gemma":0.00003362425,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001455089,"about_ca_topic_score_gemma":0.0001705767,"domain_scores_codex":[0.9972547,0.0002798292,0.0009324792,0.0007204844,0.0004930609,0.0003194367],"domain_scores_gemma":[0.997798,0.0001680329,0.00106374,0.0006323782,0.0002752453,0.00006256801],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008395126,0.002940769,0.7396737,0.00001516719,0.003229113,0.000008894463,0.0009894586,0.01457651,0.03228154,0.0311361,0.001213508,0.1730957],"study_design_scores_gemma":[0.001795205,0.0003443477,0.9679455,0.0001074213,0.001260483,0.000001470668,0.002222036,0.01491624,0.005601217,0.005055629,0.0003164061,0.0004340168],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.872423,0.00002130434,0.1238204,0.002378018,0.0004895295,0.0003832444,0.000006177508,0.0003270844,0.0001512815],"genre_scores_gemma":[0.9976878,0.00001268865,0.0008575452,0.00005762153,0.00004669572,0.0001702266,0.0002715995,0.00002165047,0.0008742416],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2282718,"threshold_uncertainty_score":0.999998,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07739575197667053,"score_gpt":0.4167174500716406,"score_spread":0.3393216980949701,"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."}}