{"id":"W4308552780","doi":"10.2196/39570","title":"Using a Proximity-Detection Technology to Nudge for Physical Distancing in a Swedish Workplace During the COVID-19 Pandemic: Retrospective Case Study","year":2022,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"COVID-19 Digital Contact Tracing","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Wearable computer; Wearable technology; Context (archaeology); Distancing; Computer science; Coronavirus disease 2019 (COVID-19); Data science; Human–computer interaction; Internet privacy; Infectious disease (medical specialty); 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":[0.002660743,0.001107145,0.0008307797,0.002434123,0.007783548,0.002448317,0.001925202,0.003781911,0.00232257],"category_scores_gemma":[0.01081154,0.0009764434,0.0009581549,0.001228096,0.00361256,0.002188752,0.0038367,0.003103656,0.000660023],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002559817,"about_ca_system_score_gemma":0.002165162,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008190698,"about_ca_topic_score_gemma":0.01574969,"domain_scores_codex":[0.995193,0.002404254,0.0004117969,0.0004913553,0.0007019858,0.0007976176],"domain_scores_gemma":[0.9931837,0.003454757,0.001223624,0.0003960289,0.0006772077,0.001064696],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.0002557581,0.00121931,0.08715889,0.001111727,0.00006129607,0.2843423,0.5929213,0.0004105876,0.003348205,0.002102145,0.002947531,0.02412084],"study_design_scores_gemma":[0.00001241051,0.001084316,0.02631461,0.0009016396,0.00006446404,0.1542461,0.7947831,0.000662757,0.002362119,0.0006763822,0.01871802,0.0001740121],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9905882,0.001176807,0.003212232,0.001278549,0.00008041159,0.0002520414,0.0001324206,0.00002569594,0.003253522],"genre_scores_gemma":[0.9926784,0.001685468,0.001984456,0.000659517,0.00006697825,0.000141223,0.00007249165,0.00003185099,0.002679665],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008190698,"threshold_uncertainty_score":0.01857281,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1122398956403116,"score_gpt":0.4565361941767647,"score_spread":0.3442962985364531,"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."}}