{"id":"W4416589406","doi":"10.2196/73903","title":"Interpretable Machine Learning Models for Analyzing Determinants Affecting the Use of mHealth Apps Among Family Caregivers of Patients With Stroke in Chinese Communities: Cross-Sectional Survey Study","year":2025,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Technology Use by Older Adults","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"mHealth; Popularity; Family caregivers; Software; Focus group; Focus (optics); Mobile device","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":[0.004297831,0.0009458682,0.0006504969,0.002125763,0.0006418368,0.001057429,0.0006750138,0.0007195728,0.001374315],"category_scores_gemma":[0.01309281,0.0003129838,0.001460145,0.001228689,0.0003362521,0.000882157,0.0006625829,0.0007851738,0.0001544896],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009465502,"about_ca_system_score_gemma":0.001508711,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01764013,"about_ca_topic_score_gemma":0.01143417,"domain_scores_codex":[0.9987715,0.0006618656,0.0001051363,0.0001931175,0.000116313,0.0001519967],"domain_scores_gemma":[0.9948258,0.003778569,0.0005537952,0.0002391251,0.0004243883,0.0001781901],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001135308,0.0003878769,0.9735035,0.00004997168,0.0001912126,0.0001538178,0.0006230643,0.01091353,0.0001707727,0.0002848098,0.0003235379,0.01328433],"study_design_scores_gemma":[0.00003166143,0.000664727,0.3891217,0.00009122374,0.0003255925,0.0002482433,0.003322531,0.6038108,0.0003058883,0.001597057,0.0004436719,0.00003682611],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9940381,0.0001074084,0.005175605,0.000141885,0.000009416437,0.00006405749,0.0002319306,0.00001785836,0.0002137866],"genre_scores_gemma":[0.9969868,0.00008582538,0.002469231,0.00001793892,0.000008123982,0.00007515651,0.0002393682,0.000001940584,0.0001156161],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01764013,"threshold_uncertainty_score":0.03507489,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07469736279895907,"score_gpt":0.382777682214278,"score_spread":0.308080319415319,"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."}}