{"id":"W4388880810","doi":"10.2196/46347","title":"Investigating Receptivity and Affect Using Machine Learning: Ecological Momentary Assessment and Wearable Sensing Study","year":2023,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Digital Mental Health Interventions","field":"Psychology","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute on Drug Abuse; National Institute on Aging","keywords":"mHealth; Affect (linguistics); Receptivity; Mood; Wearable computer; Applied psychology; Wearable technology; Intrusiveness; Psychology; Computer science; Social psychology; Medicine; Communication; Psychological intervention","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":[0.005507866,0.0003663088,0.0003627161,0.0005603574,0.000501801,0.0009657462,0.0003127682,0.0005346962,0.0008271486],"category_scores_gemma":[0.008487334,0.0002972032,0.0006379854,0.0004296059,0.0006376958,0.0005763986,0.000662127,0.0006598011,0.0002725337],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00022422,"about_ca_system_score_gemma":0.0005156482,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006812484,"about_ca_topic_score_gemma":0.001109239,"domain_scores_codex":[0.9979632,0.001327705,0.0001424527,0.0002004866,0.0002531297,0.000113116],"domain_scores_gemma":[0.9932885,0.003354705,0.001403672,0.0007333061,0.0007158861,0.0005040735],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007545816,0.003200476,0.9539958,0.0002109161,0.0003015333,0.0001330996,0.004807922,0.001093944,0.005125024,0.0003823151,0.0003252014,0.02966908],"study_design_scores_gemma":[0.00009527521,0.004232534,0.9841994,0.0000453874,0.0001148739,0.0003297757,0.002296807,0.004692308,0.002242848,0.0006186042,0.00109267,0.00003954763],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9959497,0.00007964284,0.003005547,0.00005954521,0.000009314127,0.0002207652,0.00007900927,0.000009096213,0.0005874629],"genre_scores_gemma":[0.996417,0.00007004139,0.002830627,0.00007833663,0.00001673908,0.0003050417,0.00007134883,0.000003020107,0.0002077807],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005507866,"threshold_uncertainty_score":0.02912873,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1689948939031775,"score_gpt":0.4973081764978758,"score_spread":0.3283132825946983,"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."}}