{"id":"W4225007751","doi":"10.2196/34606","title":"Designing Tangibles to Support Emotion Logging for Older Adults: Development and Usability Study","year":2022,"lang":"en","type":"article","venue":"JMIR Human Factors","topic":"Innovative Human-Technology Interaction","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council","keywords":"Usability; Psychology; Human–computer interaction; Logging; Pluralistic walkthrough; Computer science; Applied psychology; Geography","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":[],"consensus_categories":[],"category_scores_codex":[0.0005944457,0.0001672462,0.0001683324,0.0004502901,0.0009839876,0.000104485,0.0004667774,0.00003842248,0.00007154569],"category_scores_gemma":[0.00003779964,0.0001705947,0.00003003277,0.0003608431,0.00003061402,0.0003941663,0.000544565,0.0002111722,0.000005169522],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003792898,"about_ca_system_score_gemma":0.00004280001,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001220807,"about_ca_topic_score_gemma":0.00002482752,"domain_scores_codex":[0.9984605,0.00008067296,0.0003301085,0.0005690942,0.0002731736,0.0002864802],"domain_scores_gemma":[0.9993178,0.00007452567,0.0001329255,0.0003147411,0.0001154653,0.00004453847],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001048481,0.002578039,0.5620105,0.0001819562,0.0001950148,0.00003026721,0.3204242,0.0001226874,0.01519917,0.01217153,0.003327673,0.08365411],"study_design_scores_gemma":[0.001622142,0.002707961,0.9084303,0.00004593987,0.00001148499,0.00001420552,0.03907438,0.0003649432,0.04058939,0.000975711,0.005416318,0.0007472204],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9142531,0.000001817685,0.08367085,0.00007146593,0.0002345459,0.001470694,0.000002862287,0.0002435691,0.00005113903],"genre_scores_gemma":[0.9909727,2.127421e-8,0.008021642,0.00009502586,0.0000170241,0.0007186312,0.00001556302,0.00001297938,0.0001464091],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3464198,"threshold_uncertainty_score":0.7568137,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04223038633709397,"score_gpt":0.3241057179647601,"score_spread":0.2818753316276661,"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."}}