{"id":"W4200170737","doi":"10.1093/heapol/czab149","title":"Using gender analysis matrixes to integrate a gender lens into infectious diseases outbreaks research","year":2021,"lang":"en","type":"article","venue":"Health Policy and Planning","topic":"Gender Roles and Identity Studies","field":"Social Sciences","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Canadian Institutes of Health Research; Leverhulme Trust; Bill and Melinda Gates Foundation","keywords":"Pandemic; Gender analysis; Outbreak; Infectious disease (medical specialty); Neglect; Disease; Meaning (existential); Public health; Psychology; Medicine; Coronavirus disease 2019 (COVID-19); Environmental health; Political science; Virology; Psychiatry; Pathology","routes":{"ca_aff":true,"ca_fund":true,"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":["sts"],"consensus_categories":[],"category_scores_codex":[0.001226361,0.000118891,0.0003139296,0.0005129271,0.002713448,0.0002605004,0.00009691282,0.0000726491,0.00001646441],"category_scores_gemma":[0.0008842151,0.0001178264,0.00008662052,0.002207002,0.00007764041,0.0001568253,0.0001765372,0.000229803,0.00001226616],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000312067,"about_ca_system_score_gemma":0.0008893425,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1282703,"about_ca_topic_score_gemma":0.02577729,"domain_scores_codex":[0.9974757,0.0007531436,0.0002515702,0.0003540009,0.0004332478,0.0007323609],"domain_scores_gemma":[0.9989489,0.0001656634,0.00006404424,0.0001568768,0.000283197,0.0003813121],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"qualitative","study_design_scores_codex":[0.00002267676,0.0000818521,0.5483736,0.0003122137,0.0006033703,0.00004833897,0.4166712,0.001777222,0.00002724612,0.01836948,0.009537762,0.004174969],"study_design_scores_gemma":[0.0005514625,0.0001085694,0.3557339,0.000162326,0.0003423395,0.00001395313,0.4875359,0.0008688133,0.00001538382,0.02195909,0.1320505,0.000657834],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9369562,0.01353122,0.003762967,0.03429138,0.0003793223,0.0004504864,0.00007666201,0.0001458976,0.01040588],"genre_scores_gemma":[0.9945915,0.001230418,0.0004343477,0.002671702,0.0006898023,0.00001037833,0.000009506287,0.00001008513,0.0003522896],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1926398,"threshold_uncertainty_score":0.9985849,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3254064363400064,"score_gpt":0.5374809842272652,"score_spread":0.2120745478872588,"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."}}