{"id":"W2564037277","doi":"10.2196/mhealth.6705","title":"The Mobile Phone Affinity Scale: Enhancement and Refinement","year":2016,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Impact of Technology on Adolescents","field":"Social Sciences","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute on Drug Abuse; National Institutes of Health","keywords":"Mobile phone; Computer science; Scale (ratio); Telecommunications; Geography; Cartography","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":[],"consensus_categories":[],"category_scores_codex":[0.003109629,0.0004331434,0.0005253762,0.001351851,0.0005033603,0.0008328981,0.0006974895,0.0006445355,0.003746283],"category_scores_gemma":[0.009650572,0.0005002383,0.001374551,0.0008575496,0.0003242676,0.00107012,0.001445044,0.001203188,0.0008025595],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004982043,"about_ca_system_score_gemma":0.0009548823,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001596273,"about_ca_topic_score_gemma":0.004500516,"domain_scores_codex":[0.9979857,0.000572146,0.0004443451,0.0001502733,0.0007128306,0.0001346337],"domain_scores_gemma":[0.9953648,0.001605247,0.0009805488,0.0003316177,0.00136452,0.000353156],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008845162,0.001106423,0.6705583,0.001020614,0.0003905079,0.000397184,0.004750156,0.0008310267,0.002680462,0.00136876,0.009180184,0.3068318],"study_design_scores_gemma":[0.0001620803,0.0008104682,0.9768673,0.0002467535,0.0001377423,0.0007252768,0.001223442,0.001680213,0.0005587238,0.0008784215,0.0166545,0.00005513055],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9730944,0.0007586018,0.006338757,0.0006244393,0.0001239877,0.004471016,0.002693366,0.0001843979,0.01171103],"genre_scores_gemma":[0.9381812,0.001333073,0.04445794,0.0004191608,0.0001094406,0.007614178,0.003020799,0.00005821627,0.004805949],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.003746283,"threshold_uncertainty_score":0.01644552,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03408425625091747,"score_gpt":0.3875012817327481,"score_spread":0.3534170254818306,"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."}}