{"id":"W4387883867","doi":"10.2196/47294","title":"Leveraging mHealth to Mitigate the Impact of COVID-19 in Black American Communities: Qualitative Analysis","year":2023,"lang":"en","type":"article","venue":"JMIR Human Factors","topic":"Mobile Health and mHealth Applications","field":"Health Professions","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Center for Advancing Translational Sciences; National Institutes of Health; National Heart, Lung, and Blood Institute; Georgia Clinical and Translational Science Alliance","keywords":"mHealth; Thematic analysis; Misinformation; Health equity; Focus group; Community-based participatory research; Population; Medicine; Gerontology; Public health; Family medicine; Qualitative research; Psychology; Participatory action research; Psychological intervention; Environmental health; Nursing; Political science; Sociology","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":[0.02370977,0.0005546605,0.0006593764,0.002652835,0.008581963,0.003148036,0.001836682,0.001184741,0.003501693],"category_scores_gemma":[0.0273925,0.0005138605,0.0005070934,0.002568645,0.006536803,0.003167743,0.005551573,0.002089326,0.0003010182],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008218799,"about_ca_system_score_gemma":0.01201488,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02028929,"about_ca_topic_score_gemma":0.03131982,"domain_scores_codex":[0.9903626,0.007226438,0.0002504209,0.0004063681,0.0007256576,0.001028561],"domain_scores_gemma":[0.9739387,0.02063764,0.001102404,0.0004378882,0.002750292,0.001133089],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.00003301936,0.00005395249,0.002331776,0.0003704331,0.000005466236,0.0003400726,0.9861486,0.00005400309,0.0006634482,0.001825825,0.0009433422,0.007230056],"study_design_scores_gemma":[0.000004827546,0.0000218362,0.001084577,0.0003752435,0.000004652126,0.00004888136,0.9929627,0.0001187275,0.0002649292,0.0005713467,0.004534008,0.000008358453],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9675435,0.001228089,0.009948792,0.006369473,0.0001316257,0.002746596,0.0009955516,0.00003711808,0.01099919],"genre_scores_gemma":[0.9769016,0.001795822,0.009841203,0.001953054,0.00002983248,0.004248226,0.0003517566,0.00005497691,0.004823591],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02370977,"threshold_uncertainty_score":0.1253908,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2279631458612719,"score_gpt":0.5936612400372648,"score_spread":0.365698094175993,"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."}}