{"id":"W4400584736","doi":"10.1108/jkm-08-2023-0711","title":"Technostress and disengagement from knowledge sharing: insights from pre-pandemic and mid-pandemic data sets","year":2024,"lang":"en","type":"article","venue":"Journal of Knowledge Management","topic":"Technostress in Professional Settings","field":"Psychology","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland; HEC Montréal","funders":"","keywords":"Technostress; Pandemic; Knowledge management; Disengagement theory; Knowledge sharing; Coronavirus disease 2019 (COVID-19); Computer science; Business; Psychology; Medicine","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007734927,0.0004286965,0.0005481878,0.0006205917,0.0002030444,0.0002117686,0.001547718,0.0002709255,0.000241635],"category_scores_gemma":[0.00009164624,0.0003472789,0.0000908235,0.0003776787,0.0002070344,0.0004520931,0.003685476,0.001151954,0.0001130797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001779178,"about_ca_system_score_gemma":0.00004504499,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005143448,"about_ca_topic_score_gemma":0.0001116429,"domain_scores_codex":[0.9970049,0.0001850838,0.0009952278,0.001032414,0.000383786,0.000398544],"domain_scores_gemma":[0.9974964,0.000651576,0.0003677054,0.001153655,0.00009122655,0.0002394378],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003739958,0.0009410773,0.08645611,0.00072632,0.003209181,0.0009876796,0.01294137,0.000001899778,0.001130318,0.01067346,0.07443234,0.8081263],"study_design_scores_gemma":[0.005713101,0.0005040518,0.3499125,0.009288655,0.002583708,0.0001749063,0.00982831,0.00231272,0.0004674768,0.061113,0.5565936,0.001507961],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8339703,0.1456155,0.0005796585,0.0003723839,0.004287044,0.0006000905,0.000245658,0.0003059167,0.0140235],"genre_scores_gemma":[0.9918975,0.002037415,0.001613845,0.00006093526,0.0006097982,0.000033379,0.00009203501,0.00007691859,0.003578211],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8066183,"threshold_uncertainty_score":0.9998979,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05892012277804705,"score_gpt":0.3849463369624211,"score_spread":0.3260262141843741,"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."}}