{"id":"W2859925763","doi":"10.2196/11140","title":"Expanding Opportunities for Professional Development: Utilization of Twitter by Early Career Women in Academic Medicine and Science","year":2018,"lang":"en","type":"article","venue":"JMIR Medical Education","topic":"Diversity and Career in Medicine","field":"Social Sciences","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Academic medicine; Economic shortage; Perspective (graphical); Public relations; Gender equity; Social media; Medical education; Pipeline (software); Equity (law); Career development; Sociology; Psychology; Political science; Medicine; Computer science; Social science","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.004665816,0.0003305653,0.0002784554,0.0009700629,0.005270861,0.006162669,0.0006797826,0.001432055,0.009321662],"category_scores_gemma":[0.01372379,0.0002844127,0.000395744,0.001044684,0.001577447,0.005821554,0.006652501,0.00166199,0.001473478],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001097711,"about_ca_system_score_gemma":0.002132175,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002858624,"about_ca_topic_score_gemma":0.009095083,"domain_scores_codex":[0.995805,0.0030091,0.0001300076,0.0001797932,0.0003472854,0.0005287502],"domain_scores_gemma":[0.9925184,0.004327378,0.0008910075,0.000240476,0.0005209974,0.001501687],"domain_codex":null,"domain_gemma":"incentives","domain_candidate":"incentives","domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.0001687036,0.0001692219,0.08158244,0.0008452451,0.00006001925,0.001653014,0.5155946,0.00007978436,0.002803271,0.009200197,0.04317237,0.3446711],"study_design_scores_gemma":[0.00002632438,0.0002353422,0.03721014,0.001015375,0.000065028,0.001257312,0.6730486,0.0004964905,0.0008087507,0.004576762,0.2811692,0.00009061609],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7346663,0.005043142,0.005767179,0.1107143,0.0017478,0.0002430368,0.000461358,0.0001715401,0.1411852],"genre_scores_gemma":[0.9640669,0.004592155,0.003808856,0.01097175,0.000561281,0.0001660405,0.0001294519,0.00007574887,0.01562783],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9953342,"threshold_uncertainty_score":0.03118408,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1648088175892491,"score_gpt":0.437415326323943,"score_spread":0.2726065087346938,"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."}}