{"id":"W3028364391","doi":"10.1186/s40900-020-00203-8","title":"Identifying best approaches for engaging patients and family members in health informatics initiatives: a case study of the Group Priority Sort technique","year":2020,"lang":"en","type":"article","venue":"Research Involvement and Engagement","topic":"Mobile Health and mHealth Applications","field":"Health Professions","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Shores Centre for Mental Health Sciences; Canada Health Infoway; University of Victoria; McMaster University; Western University; Wilfrid Laurier University; University of Ottawa; University of Waterloo; University of Toronto; Institute for Work & Health; Centre for Addiction and Mental Health","funders":"Canadian Institutes of Health Research","keywords":"CLARITY; Context (archaeology); sort; Health care; Variety (cybernetics); Resource (disambiguation); Informatics; Medicine; Public relations; Medical education; Computer science; Political science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.05968615,0.0011709,0.0007444684,0.002879117,0.02536592,0.005752063,0.005211476,0.005484016,0.004336799],"category_scores_gemma":[0.0647237,0.0009232329,0.001538913,0.002320456,0.01158338,0.008601866,0.01432199,0.006388409,0.0008923851],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006823189,"about_ca_system_score_gemma":0.0147576,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004034309,"about_ca_topic_score_gemma":0.01024844,"domain_scores_codex":[0.9127095,0.07677659,0.001447313,0.001916562,0.003674357,0.003475628],"domain_scores_gemma":[0.939581,0.0428347,0.003962874,0.003552069,0.004468507,0.005600825],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0001810586,0.001337147,0.007660949,0.0007566633,0.00003666748,0.007022852,0.9002382,0.0004425594,0.00157429,0.008934032,0.005169541,0.06664606],"study_design_scores_gemma":[0.0001075085,0.000896983,0.002194819,0.000620939,0.00004476108,0.003608039,0.9443699,0.001197133,0.001566993,0.0055081,0.03979448,0.00009038556],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8591855,0.001104654,0.08339646,0.02472032,0.0004463446,0.004587993,0.0001280014,0.0002062795,0.02622454],"genre_scores_gemma":[0.8653454,0.001394818,0.1214205,0.003509961,0.0001088691,0.003119426,0.00008914495,0.0001133336,0.00489864],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05968615,"threshold_uncertainty_score":0.3156542,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.491195531856995,"score_gpt":0.5089391451864642,"score_spread":0.01774361332946922,"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."}}