{"id":"W3144411754","doi":"10.1177/07334648211004687","title":"Age Knows No Bounds: A Latent Content Analysis of Social Media Comments Toward Older Adults’ Engagement in Sports Activities","year":2021,"lang":"en","type":"article","venue":"Journal of Applied Gerontology","topic":"Technology Use by Older Adults","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Thompson Rivers University","funders":"University of Santo Tomas","keywords":"Content analysis; Social media; Psychology; Set (abstract data type); Content (measure theory); Qualitative analysis; Social engagement; Media content; Social psychology; Qualitative research; Applied psychology; Gerontology; Sociology; Computer science; Multimedia; Medicine; World Wide Web; 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":[0.006682199,0.0002723685,0.000248308,0.004136073,0.001247678,0.002585266,0.0005121346,0.0003928971,0.0017249],"category_scores_gemma":[0.02467354,0.000195238,0.0004130243,0.003132452,0.001493484,0.00272567,0.002515089,0.0005167609,0.0003220144],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001608724,"about_ca_system_score_gemma":0.001919248,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005920698,"about_ca_topic_score_gemma":0.009963767,"domain_scores_codex":[0.9970288,0.001569422,0.0002898873,0.0003704799,0.0004952921,0.000246169],"domain_scores_gemma":[0.9835856,0.01027275,0.002673596,0.0007196988,0.002418054,0.0003302984],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0004346369,0.0002339651,0.3647341,0.0009475831,0.00008571854,0.0003756523,0.5148097,0.0003230553,0.006773505,0.008271125,0.00302831,0.09998264],"study_design_scores_gemma":[0.00003090493,0.0002471373,0.4145601,0.0005837063,0.0001186007,0.0002794039,0.5533221,0.007247223,0.002728304,0.006384205,0.01441352,0.00008469788],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9776021,0.0001350601,0.01542529,0.0003895981,0.00002725603,0.0007660121,0.001438072,0.0000508092,0.00416585],"genre_scores_gemma":[0.9829733,0.00009980729,0.01357768,0.00009595563,0.00002178243,0.001224196,0.0009852416,0.00002977134,0.0009921421],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006682199,"threshold_uncertainty_score":0.0353393,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05681063618167226,"score_gpt":0.3097780810790941,"score_spread":0.2529674448974218,"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."}}