{"id":"W4280524153","doi":"10.2196/preprints.38340","title":"Deconstructing TikTok Videos on Mental Health: Cross-sectional, Descriptive Content Analysis (Preprint)","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Digital Mental Health Interventions","field":"Psychology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Social media; Mental health; Content analysis; Harm; Suicidal ideation; Psychology; Descriptive statistics; Content (measure theory); Descriptive research; Cross-sectional study; Medicine; Social psychology; Suicide prevention; Psychiatry; Computer science; Poison control; Sociology; World Wide Web; Medical emergency; Social science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008184128,0.0002894908,0.0002921991,0.00380026,0.001232054,0.00230652,0.0004473361,0.0005074082,0.003245371],"category_scores_gemma":[0.0358058,0.0003807277,0.0003660476,0.00395177,0.001393462,0.00200542,0.001753697,0.0007702477,0.0006114778],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00261664,"about_ca_system_score_gemma":0.002573212,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008339311,"about_ca_topic_score_gemma":0.015002,"domain_scores_codex":[0.9971493,0.001210078,0.0004008739,0.0003112873,0.000630799,0.0002977003],"domain_scores_gemma":[0.9725175,0.01687317,0.003720093,0.0008380326,0.005663924,0.0003873141],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.0003031989,0.0008951378,0.3950535,0.001812101,0.00007789132,0.0004023302,0.5117034,0.0001917392,0.003292918,0.002355368,0.009670636,0.07424179],"study_design_scores_gemma":[0.00003190402,0.0003538207,0.5902285,0.001140945,0.00006403057,0.0002348719,0.3859333,0.0007665546,0.001944676,0.0005647858,0.01868803,0.00004871839],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9905688,0.0001718438,0.001491982,0.0003022439,0.00003366751,0.001831459,0.00252532,0.00002343111,0.003051355],"genre_scores_gemma":[0.9726607,0.0005632683,0.007719342,0.0006894454,0.00007298499,0.01014523,0.004862851,0.00008628429,0.003200002],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008339311,"threshold_uncertainty_score":0.04328227,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2067686349446167,"score_gpt":0.4578843117896829,"score_spread":0.2511156768450662,"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."}}