{"id":"W4387412204","doi":"10.2196/50814","title":"The Use of Machine Translation for Outreach and Health Communication in Epidemiology and Public Health: Scoping Review","year":2023,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Leibniz-Gemeinschaft","keywords":"Outreach; Generalizability theory; Scopus; Systematic review; Public health; Population; Population health; Knowledge translation; MEDLINE; Computer science; Medical education; Medicine; Knowledge management; Psychology; Environmental health; Political science; Nursing","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.0558478,0.002293517,0.007280839,0.04045524,0.00230983,0.007480156,0.003069757,0.004728988,0.005741284],"category_scores_gemma":[0.2246159,0.001732685,0.008021595,0.03703396,0.00338255,0.008186183,0.004992078,0.002434652,0.001020881],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006795153,"about_ca_system_score_gemma":0.03824588,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009070852,"about_ca_topic_score_gemma":0.0153311,"domain_scores_codex":[0.9353827,0.02637564,0.0258795,0.002364217,0.009223085,0.0007748861],"domain_scores_gemma":[0.7209584,0.2320951,0.02222319,0.005406594,0.01860102,0.0007156107],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","study_design_scores_codex":[0.00007958729,0.00002473377,0.000612075,0.8435929,0.002239859,0.0001613032,0.00106017,0.0002223722,0.0001834346,0.001665485,0.003337222,0.1468209],"study_design_scores_gemma":[0.00002077187,0.00004410514,0.00067433,0.9652349,0.00552157,0.0001650924,0.0004334928,0.00009679669,0.0001448424,0.0007236786,0.02691901,0.00002156442],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0006660232,0.9924434,0.001740944,0.001201332,0.0005481798,0.001553722,0.0004230612,0.00002792909,0.001395468],"genre_scores_gemma":[0.007053876,0.9854395,0.003494101,0.0006532307,0.0002330824,0.002607949,0.0003135048,0.00001933909,0.0001855091],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.0558478,"threshold_uncertainty_score":0.295355,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2648480732460546,"score_gpt":0.4451489610399774,"score_spread":0.1803008877939228,"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."}}