{"id":"W3005143449","doi":"10.24251/hicss.2020.747","title":"Techno(Stress) and Techno(Distress): Validation of a Specific TechnoStressors Index (TSI) Among Quebec Lawyers","year":2020,"lang":"en","type":"article","venue":"Proceedings of the ... Annual Hawaii International Conference on System Sciences/Proceedings of the Annual Hawaii International Conference on System Sciences","topic":"Technostress in Professional Settings","field":"Psychology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Technostress; Stressor; Context (archaeology); Distress; Psychology; Scale (ratio); Applied psychology; Relevance (law); Sample (material); Social psychology; Clinical psychology; Political science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.003746517,0.000278644,0.0002302958,0.001778627,0.001916609,0.001587319,0.0008285014,0.0003997398,0.002814096],"category_scores_gemma":[0.01487779,0.0001958264,0.0002798228,0.001846119,0.001211534,0.0004566736,0.0009350075,0.0007148692,0.0001942306],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01199226,"about_ca_system_score_gemma":0.008462214,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.6848779,"about_ca_topic_score_gemma":0.8174556,"domain_scores_codex":[0.9981977,0.0003750103,0.0001344044,0.0001811589,0.0008713889,0.000240209],"domain_scores_gemma":[0.9876909,0.003003847,0.002303639,0.0005388221,0.005155487,0.001307366],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007045331,0.0001969125,0.9446073,0.00006450968,0.00003393643,0.00008503447,0.01569835,0.000266438,0.001233546,0.0002714121,0.001370562,0.03610141],"study_design_scores_gemma":[0.000004144496,0.00005591709,0.9946423,0.00001874149,0.000005580114,0.00001435473,0.003351529,0.0004074407,0.0002480437,0.00003387883,0.001208799,0.000009286405],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9956183,0.00005137477,0.0006614822,0.000188036,0.000008091774,0.0001751656,0.0003702221,0.00001408083,0.002913203],"genre_scores_gemma":[0.9963378,0.00006325006,0.001254629,0.00005173725,0.000004865275,0.0001989594,0.0003524271,0.000005230267,0.001731061],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3151221,"threshold_uncertainty_score":0.6339558,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04167573426817833,"score_gpt":0.3095039682476374,"score_spread":0.2678282339794591,"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."}}