{"id":"W4379620397","doi":"10.1177/2327857923121034","title":"Applying Failure Mode Effects Analysis (FMEA) to Improve Choking Risk Prevention in a Mental Health Setting: Analysis Outcomes and Lessons Learned on Human Factors Collaboration","year":2023,"lang":"en","type":"article","venue":"Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care","topic":"Occupational Health and Safety Research","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Shores Centre for Mental Health Sciences; CARE Canada","funders":"","keywords":"Thematic analysis; Choking; Process (computing); Failure mode and effects analysis; Work (physics); Quality (philosophy); Health care; Nursing; Medicine; Risk analysis (engineering); Engineering; Qualitative research; Computer science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.001599635,0.0002851641,0.0007237794,0.001635474,0.001440214,0.00007095149,0.0003299822,0.0001715289,0.00000832445],"category_scores_gemma":[0.0003274826,0.0002313428,0.0001795042,0.001605173,0.0000427663,0.0001908547,0.0002627595,0.0007557712,0.000001759782],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002040368,"about_ca_system_score_gemma":0.0002687733,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.007025638,"about_ca_topic_score_gemma":0.02255344,"domain_scores_codex":[0.996771,0.00020663,0.001151615,0.0006700468,0.000602045,0.0005986686],"domain_scores_gemma":[0.9977162,0.0006461427,0.0009457464,0.0001671315,0.0002533712,0.00027136],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002444839,0.00007189757,0.9766995,0.0006681824,0.0002163763,9.307858e-8,0.01633585,0.002048417,0.0003625283,0.002460819,0.0001095533,0.0007823322],"study_design_scores_gemma":[0.000964311,0.0003661954,0.9832188,0.0004253497,0.00005390868,3.341639e-8,0.01171381,0.002445652,0.0001328403,0.0002843016,0.0002215977,0.0001732141],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9871261,0.00002804021,0.000005222766,0.009203347,0.0002878775,0.002700842,0.0004862515,0.00003882456,0.0001235473],"genre_scores_gemma":[0.9982266,0.0002120729,0.00006345147,0.0004234305,0.00006516882,0.0004550806,0.0004158382,0.00002770427,0.0001106869],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01552781,"threshold_uncertainty_score":0.9998598,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05808366239686049,"score_gpt":0.4582100960948262,"score_spread":0.4001264336979657,"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."}}