{"id":"W4396219292","doi":"10.4103/abhs.abhs_9_24","title":"Use of mind genomics for public health and wellbeing: Lessons from COVID 19 pandemic","year":2024,"lang":"en","type":"article","venue":"Advances in Biomedical and Health Sciences","topic":"COVID-19 and Mental Health","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Toronto Metropolitan University","funders":"","keywords":"Mindset; Snowball sampling; Distancing; Pandemic; Government (linguistics); Public health; Social distance; Psychology; Public policy; Coronavirus disease 2019 (COVID-19); Public relations; Sample (material); Medical education; Applied psychology; Medicine; Political science; Nursing; Infectious disease (medical specialty); Disease; Computer science; Law","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004959475,0.0003630705,0.0003508426,0.0005184349,0.001554888,0.001578421,0.0007664568,0.0009314967,0.002576016],"category_scores_gemma":[0.009327194,0.000197798,0.0005503296,0.000336466,0.001697663,0.002201436,0.002668131,0.002549266,0.0003467562],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001588146,"about_ca_system_score_gemma":0.00307614,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003884148,"about_ca_topic_score_gemma":0.01133424,"domain_scores_codex":[0.9981722,0.001390632,0.00004115273,0.00007435399,0.0001356607,0.0001860649],"domain_scores_gemma":[0.9953353,0.003052699,0.0002087139,0.0001389739,0.0003876303,0.0008767201],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.000244859,0.001565251,0.0541345,0.002307847,0.00007105615,0.0012667,0.1834736,0.0007087812,0.001669626,0.003956755,0.03143983,0.7191612],"study_design_scores_gemma":[0.0001915937,0.003272645,0.1936996,0.008335253,0.0002267307,0.001782771,0.5640459,0.002990444,0.002746534,0.02976996,0.1927546,0.0001839354],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7713872,0.01058484,0.004366645,0.1943704,0.001506684,0.0002752072,0.0001719723,0.0001340934,0.01720298],"genre_scores_gemma":[0.9667593,0.01179052,0.006300647,0.01166325,0.0003963081,0.000203479,0.00009194806,0.00003563291,0.00275891],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004959475,"threshold_uncertainty_score":0.02622855,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3577231492529861,"score_gpt":0.5387234121684479,"score_spread":0.1810002629154618,"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."}}