{"id":"W3160098319","doi":"10.2196/23461","title":"Digital Phenotypes for Understanding Individuals' Compliance With COVID-19 Policies and Personalized Nudges: Longitudinal Observational Study","year":2021,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Digital Mental Health Interventions","field":"Psychology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Nudge theory; Context (archaeology); Pandemic; Compliance (psychology); Social distance; Behavioural sciences; Telehealth; Observational study; Psychology; Personalization; Coronavirus disease 2019 (COVID-19); Business; Applied psychology; Internet privacy; Telemedicine; Medicine; Social psychology; Computer science; Health care; Marketing; Political science; Geography","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":[0.003472464,0.0002575211,0.0003091116,0.000719242,0.0009596564,0.001170925,0.0005772202,0.0007581587,0.003599768],"category_scores_gemma":[0.01192208,0.0003621101,0.0007737963,0.0008165762,0.0004106081,0.001250169,0.001042078,0.001224058,0.0007286331],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004377098,"about_ca_system_score_gemma":0.000737353,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007733075,"about_ca_topic_score_gemma":0.01007453,"domain_scores_codex":[0.9985806,0.0006518934,0.0001720743,0.000250493,0.0001781954,0.0001667573],"domain_scores_gemma":[0.9932802,0.001759828,0.002543863,0.001096161,0.0007235123,0.0005964019],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002807236,0.0007214858,0.9907352,0.00006233721,0.0001004911,0.00006594024,0.001065981,0.00008346852,0.0001845867,0.000107718,0.0009436959,0.005648333],"study_design_scores_gemma":[0.00003091672,0.000965998,0.9946919,0.00006714118,0.00008160081,0.0001277518,0.001673685,0.0006755716,0.0001701481,0.0001638031,0.001327074,0.00002426913],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9963682,0.0001746014,0.0006362846,0.0001476315,0.00001809694,0.0001540496,0.00164569,0.00001213378,0.0008434497],"genre_scores_gemma":[0.9964102,0.0001204741,0.001106329,0.0001288329,0.00001699465,0.0002881765,0.001290079,0.00000625103,0.0006326282],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007733075,"threshold_uncertainty_score":0.01836437,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6065331611258804,"score_gpt":0.5739689054036599,"score_spread":0.03256425572222055,"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."}}