{"id":"W2956210777","doi":"10.2196/14724","title":"Reimbursement of Apps for Mental Health: Findings From Interviews","year":2019,"lang":"en","type":"article","venue":"JMIR Mental Health","topic":"Digital Mental Health Interventions","field":"Psychology","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Reimbursement; Payment; Mental health; Current Procedural Terminology; Medical prescription; Health care; Stakeholder; Medicine; Business; Family medicine; Internet privacy; Nursing; Public relations; Psychiatry; Computer science; Finance; Political 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0007671475,0.0003136952,0.0007254503,0.0001924825,0.0001983772,0.00002577208,0.0003988428,0.0001029679,0.002361114],"category_scores_gemma":[0.000004670298,0.0003180072,0.0003508177,0.0002030834,0.00007855555,0.0001927662,0.0001662186,0.0002184246,0.001176356],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001087914,"about_ca_system_score_gemma":0.0001533093,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002207326,"about_ca_topic_score_gemma":0.000508846,"domain_scores_codex":[0.9962333,0.0002150072,0.001596008,0.0006822806,0.0003770108,0.0008963411],"domain_scores_gemma":[0.9981521,0.00008482036,0.0006932198,0.0006032761,0.00003429968,0.0004323016],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001868297,0.006168115,0.01023115,0.00267824,0.0003120906,0.000001417231,0.02490309,4.048647e-7,0.0003370859,0.02678977,0.5634998,0.3632105],"study_design_scores_gemma":[0.01867232,0.02880142,0.08634707,0.006957712,0.00002425651,0.00003869781,0.02263565,0.0001101465,0.002490325,0.005129783,0.8275536,0.001239029],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9443301,0.003038409,0.0001064236,0.02369905,0.008216449,0.009686491,0.005535249,0.000129421,0.005258402],"genre_scores_gemma":[0.9810003,0.00004335695,0.001064716,0.004912685,0.0000903413,0.0008767651,0.001835665,0.00007642976,0.01009972],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3619715,"threshold_uncertainty_score":0.9999272,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0593901068852485,"score_gpt":0.4468069135958793,"score_spread":0.3874168067106308,"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."}}