{"id":"W4281253161","doi":"10.1016/j.heliyon.2022.e09458","title":"Creating standards for Canadian health data protection during health emergency – An analysis of privacy regulations and laws","year":2022,"lang":"en","type":"article","venue":"Heliyon","topic":"COVID-19 Digital Contact Tracing","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Prince Sultan University","keywords":"Data Protection Act 1998; Information privacy law; Information privacy; Privacy law; European union; Government (linguistics); Business; Privacy laws of the United States; General Data Protection Regulation; Limiting; Data sharing; Privacy by Design; Internet privacy; Data Protection Directive; Computer security; Law; Privacy policy; Political science; Engineering; European Union law; Computer science; Medicine; International trade","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02432358,0.000486359,0.0004807012,0.009468873,0.01187547,0.01543428,0.003881092,0.002746038,0.004214159],"category_scores_gemma":[0.06075696,0.0007897602,0.001369093,0.01343265,0.006957144,0.003853723,0.003798297,0.003840077,0.0006098374],"about_ca_system_candidate":true,"about_ca_system_consensus":true,"about_ca_system_score_codex":0.22243,"about_ca_system_score_gemma":0.43744,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9917898,"about_ca_topic_score_gemma":0.9905552,"domain_scores_codex":[0.9394218,0.00654601,0.002781587,0.002436091,0.04147107,0.007343436],"domain_scores_gemma":[0.9034693,0.01289138,0.003144826,0.004971397,0.07234332,0.003179861],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004278097,0.00008375796,0.02132891,0.0004237652,0.0000406487,0.0003426625,0.01407142,0.003438292,0.0010089,0.8249931,0.06873784,0.06548785],"study_design_scores_gemma":[0.00003406264,0.0000473616,0.1003198,0.0015199,0.0001099635,0.0002251494,0.02125543,0.006101587,0.003154367,0.03770199,0.8292601,0.0002702834],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.08760203,0.008888892,0.04270292,0.1282693,0.0008279458,0.002507793,0.01109768,0.0006044772,0.7174988],"genre_scores_gemma":[0.7016742,0.01115949,0.136303,0.02100364,0.0002012489,0.001039876,0.009437636,0.0003770224,0.1188038],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.77757,"threshold_uncertainty_score":0.901871,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06960113061005868,"score_gpt":0.3574895161113591,"score_spread":0.2878883855013004,"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."}}