{"id":"W3193772858","doi":"","title":"IMPACT OF COVID-19 PANDEMIC ON WELLBEING OF DOCTORS IN KASHMIR","year":2021,"lang":"en","type":"article","venue":"Annals of Medical and Health Sciences Research","topic":"COVID-19 Pandemic Impacts","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Pandemic; Medicine; Livelihood; Globe; Coronavirus disease 2019 (COVID-19); Quarter (Canadian coin); China; Workload; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Socioeconomics; Agriculture; Outbreak; Virology; Disease; Management; Geography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02185088,0.00007944695,0.0005394381,0.0007937042,0.0001107484,0.00001549602,0.0004322199,0.0001252726,0.0005656556],"category_scores_gemma":[0.008338503,0.0000673007,0.00008362871,0.001557332,0.0009631661,0.0001027625,0.0001824638,0.0004054409,0.000005983464],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001152604,"about_ca_system_score_gemma":0.003340709,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0185305,"about_ca_topic_score_gemma":0.0007891222,"domain_scores_codex":[0.9973441,0.0001724366,0.0009186361,0.0003687317,0.0006083145,0.0005877865],"domain_scores_gemma":[0.9973689,0.00137732,0.0002896879,0.000202121,0.0001016752,0.0006602519],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003620976,0.0001745427,0.972725,0.0005103298,0.00001089991,0.000007987729,0.001247621,0.00009569588,0.00002257075,0.01816767,0.001725902,0.005275601],"study_design_scores_gemma":[0.001121147,0.001958204,0.9409461,0.0005437553,5.649907e-7,0.00001150401,0.0006588051,0.003075302,0.0001501847,0.04403882,0.007349295,0.0001463341],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9603616,0.005079588,0.00003616669,0.03313918,0.00005357396,0.000134953,0.00003933333,0.000003572281,0.001152051],"genre_scores_gemma":[0.9820804,0.01609638,0.0000361975,0.001694765,0.00003034785,0.000003221465,0.000001923629,0.000003789949,0.00005299802],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03177888,"threshold_uncertainty_score":0.9982569,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6025187546433,"score_gpt":0.5790430260151217,"score_spread":0.02347572862817826,"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."}}