{"id":"W4225851417","doi":"10.1145/3512946","title":"Involving Crowdworkers with Lived Experience in Content-Development for Push-Based Digital Mental Health Tools: Lessons Learned from Crowdsourcing Mental Health Messages","year":2022,"lang":"en","type":"article","venue":"Proceedings of the ACM on Human-Computer Interaction","topic":"Digital Mental Health Interventions","field":"Psychology","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Center for Advancing Translational Sciences; National Institute of Mental Health","keywords":"Crowdsourcing; Personalization; Mental health; Digital content; Adaptation (eye); Content (measure theory); Computer science; World Wide Web; Content analysis; Internet privacy; Psychology; Knowledge management; Sociology; Psychiatry","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005164962,0.0003167932,0.0004440289,0.0003048788,0.0009858232,0.0002998974,0.001079172,0.00004580178,0.000101635],"category_scores_gemma":[0.00006363093,0.0002819071,0.000183545,0.0002729246,0.0001030465,0.0007900584,0.0006754944,0.0004759132,0.000006846549],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001817021,"about_ca_system_score_gemma":0.0001168038,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005727037,"about_ca_topic_score_gemma":0.0001124142,"domain_scores_codex":[0.9971139,0.00006492215,0.001013441,0.0007241989,0.0005011934,0.0005823447],"domain_scores_gemma":[0.998126,0.0001792059,0.001115611,0.0003555451,0.00008707782,0.0001366119],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.01310441,0.01363383,0.0502973,0.00131617,0.001025993,0.00000685256,0.3162574,0.0008149609,0.007047054,0.005575769,0.05740713,0.5335132],"study_design_scores_gemma":[0.04089565,0.03540323,0.2340808,0.02413919,0.00008168293,0.0002137975,0.5123171,0.007764216,0.05956597,0.005519046,0.07565309,0.004366213],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9759651,0.0001071865,0.0001945898,0.01974877,0.001664962,0.001750835,0.0001868568,0.0001014747,0.000280241],"genre_scores_gemma":[0.9944936,0.000001819432,0.002436873,0.001611309,0.00007465624,0.0007378668,0.0001740407,0.00005751044,0.0004123295],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.529147,"threshold_uncertainty_score":0.9999633,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.321773432908725,"score_gpt":0.4398651553978173,"score_spread":0.1180917224890923,"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."}}