{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","open_science"],"consensus_categories":[],"category_scores_codex":[0.002326393,0.0008653408,0.001076839,0.001093711,0.0008853526,0.0005827248,0.009837676,0.0005719624,0.0001705233],"category_scores_gemma":[0.0002451708,0.0005968859,0.0004328052,0.002565757,0.005606725,0.001930533,0.002051356,0.001048378,0.0000266878],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003986277,"about_ca_system_score_gemma":0.0003607306,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001418649,"about_ca_topic_score_gemma":0.00004173376,"domain_scores_codex":[0.9901226,0.00006330944,0.002195124,0.001914246,0.004867019,0.0008377747],"domain_scores_gemma":[0.9898216,0.0002503941,0.004388608,0.0004077552,0.004864022,0.0002676957],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","study_design_scores_codex":[0.0002981585,0.0002075278,0.05309116,0.0004131471,0.0001381953,0.000001874737,0.002992246,0.0000450898,0.003339164,0.9370716,0.001585785,0.000816031],"study_design_scores_gemma":[0.00111782,0.000853599,0.01263998,0.009888051,0.00007328411,0.00007473617,0.9461464,0.0003416243,0.0249954,0.00260866,0.000425965,0.0008344692],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3655856,0.00005299819,0.00002192613,0.007117317,0.001807336,0.001156435,0.001303055,0.0002687364,0.6226866],"genre_scores_gemma":[0.9979671,0.00002890589,0.0002740879,0.00008325853,0.0002687796,0.0001828086,0.000008746802,0.00004930212,0.001136996],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9431542,"threshold_uncertainty_score":0.9996483,"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."}}